Engineering, Civil

Publications

• 12th International Conference on Structural Analysis of Historical Constructions (SAHC)

  Scope

  25th anniversary edition

  During the last decades the study and conservation of historical structures has attained high technological and scientific standards. Today’s practice involves the combination of innovative non-destructive inspection technologies, sophisticated monitoring systems and advanced numerical models for structural analysis. More than ever, it is understood that the studies must be performed by interdisciplinary teams integrating wide expertise (engineering, architecture, history, archeology, geophysics, chemistry…). Moreover, the holistic nature of the studies, and the need to encompass and combine the different scales of the problem –the materials, the structures, the building aggregates, and the territory – arenow increasingly acknowledged.  Due to all this, the study of historical structures is still facing very strong challenges that can only be addressed through sound international scientific cooperation.  

  The SAHC International Conference on Structural Analysis of Historical Constructions has been created as an international, multidisciplinary forum allowing networking and exchange of knowledge on the aforementioned subjects. The last edition of the conference (SAHC2021), celebrated on September 29-30 and October 1, 2021, included the following topics: (1)history of construction and building technology; (2)inspection methods, non-destructive techniques and laboratory testing; (3) numerical modeling and structural analysis; (4) structural health monitoring; (5) repair and strengthening strategies and techniques; (6)conservation of 20th c. architectural heritage; (7) seismic analysis and retrofit; (8) vulnerability and risk analysis; and (9) interdisciplinary projects and case studies.

  This collection includes all the papers presented at SAHC 2021. 

  Scope

  25th anniversary edition

  During the last decades the study and [...]

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• 9th European Congress on Computational Methods in Applied Sciences and Engineering

  Scope

  The main event organized by ECCOMAS is its biennial European Congress, providing a rendezvous for scientists and engineers from within Europe and around the globe. The main objective of these congresses is to provide a forum for presentation and discussion of state-of-the-art developments in scientific computing applied to engineering sciences. Equal emphasis is given to basic methodologies, scientific development and industrial applications. The ECCOMAS Congress includes invited lectures, invited Special Technological Sessions (STS), contributed papers from Academy and Industry and organized Minisymposia.

  ECCOMAS Congress 2024 is a sequel to the successful previous ECCOMAS congresses in Brussels (1992), Paris (1996), Barcelona (2000), Jyväskylä (2004), Venice (2008), Vienna (2012), Crete (2016), Paris (2020, online), Oslo (2022).

  These series of ECCOMAS global meetings are complemented with more focused thematic conferences on state-of-the-art topics in computational sciences and engineering organised with the support of ECCOMAS.

   

  ISSN: 2696-6999

  Scope

  The main event organized by ECCOMAS is its biennial European Congress, providing a rendezvous for scientists and engineers from within [...]

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• Alex Barbat's articles in WoS/Scopus journals

  Scope

  Alex's Barbat Journal Papers is an online archive aimed at collecting, preserving, and disseminating digital copies of the scientific papers published on international journals by prof Alex Barbat.

  Scope

  Alex's Barbat Journal Papers is an online archive aimed at collecting, preserving, and disseminating digital copies of the scientific papers published [...]

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• Alex Barbat's chapters of books

  Scope

  Alex Barbat's Research Reports is an online archive aimed at collecting, preserving, and disseminating digital copies of the research reports written by prof Alex Barbat.

  Scope

  Alex Barbat's Research Reports is an online archive aimed at collecting, preserving, and disseminating digital copies of the research reports written by prof [...]

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• Alex Barbat's Journal Papers

  Scope

  Alex's Barbat Journal Papers is an online archive aimed at collecting, preserving, and disseminating digital copies of the scientific papers published on international journals by prof Alex Barbat.

  Scope

  Alex's Barbat Journal Papers is an online archive aimed at collecting, preserving, and disseminating digital copies of the scientific papers published [...]

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• Alex Barbat's monographs

  Scope

  Alex Barbat's Monographs is an online archive aimed at collecting, preserving, and disseminating digital copies of the monographs written by prof Alex Barbat.

  Scope

  Alex Barbat's Monographs is an online archive aimed at collecting, preserving, and disseminating digital copies of the monographs written by prof Alex [...]

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• Alex Barbat's technical and research reports

  Scope

  Alex Barbat's Technical Reports is an online archive aimed at collecting, preserving, and disseminating digital copies of the technical reports written by prof Alex Barbat.

  Scope

  Alex Barbat's Technical Reports is an online archive aimed at collecting, preserving, and disseminating digital copies of the technical reports written by prof [...]

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• Antonio Lechuga's personal collection

  Scope

  Antonio Lechuga's personal collection

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• Collection of Articles on Engineering

  Scope

  This collection gathers research and technical articles in the field of Engineering categories.

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• Collection of Engineering

  Scope

  The collection is a repository of open access articles where academic achievements of great importance in engineering science and technology can be disseminated and shared. The collection includes new focus, and updates on central issues; heuristic comments and reviews on major issues, articles, and events; research results, in the form of research articles, reviews, perspectives, short communications regarding critical issues, and so on.

  Scope

  The collection is a repository of open access articles where academic achievements of great importance in engineering science and technology can be disseminated and shared. The collection includes new focus, and updates on central issues; heuristic comments and reviews on major [...]

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• Collection of Engineering

  Scope

  The collection is a repository of open access articles in the field of engineering and applied science. The articles include experimental, theoretical and computational aspects with the objective of understanding engineering and applied sciences or giving an idea of engineering practices and processes.

  Scope

  The collection is a repository of open access articles in the field of engineering and applied science. The articles include experimental, theoretical and computational aspects with the objective of understanding engineering and applied sciences or giving an idea of engineering [...]

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• Collection of Engineering Science and Technology

  Scope

  The collection is a repository of open access articles, both theoretical and experimental high quality papers of permanent interest, in the field of engineering and applied science which aims to promote the theory and practice of technology and engineering.

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• Collection of Perspectives in Science

  Scope

  The collection is a repository of open access articles covering reports of research projects, conference proceedings and trend and niche topics.

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• Conference Lectures

  Scope

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• Dam Engineering Conference Proceedings

  Scope

  A collection about Proceedings of all Conferences on Dam Engineering

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• Edited books of the International Centre for Numerical Methods in Engineering (CIMNE)

  Scope

  The collection gathers the books edited by different authors and published by the International Centre for Numerical Methods in Engineering (CIMNE).

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• Engenharias - 2018/2 - Projeto Integrador

  Scope

  Coleção destinada aos artigos gerados como relatório final das disciplinas de Projeto Integrador dos cursos de Engenharia da Faculdade UNA Uberlândia.

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• Eugenio Oñate's Journal Papers

  Scope

  Eugenio Oñate's Journal Papers is an online archive aimed at collecting, preserving, and disseminating digital copies of the scientific papers published on international journals by prof Eugenio Oñate.

  Scope

  Eugenio Oñate's Journal Papers is an online archive aimed at collecting, preserving, and disseminating digital copies of the scientific papers published on i[...]

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• Eugenio Oñate's Monographs

  Scope

  Eugenio Oñate's Monographs is an online archive aimed at collecting, preserving, and disseminating digital copies of the monographs written by prof Eugenio Oñate.

  Scope

  Eugenio Oñate's Monographs is an online archive aimed at collecting, preserving, and disseminating [...]

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• Eugenio Oñate's Research Reports

  Scope

  Eugenio Oñate's Research Reports is an online archive aimed at collecting, preserving, and disseminating digital copies of the research reports written by prof Eugenio Oñate.

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• Eugenio Oñate's Technical Reports

  Scope

  Eugenio Oñate's Technical Reports is an online archive aimed at collecting, preserving, and disseminating digital copies of the technical reports written by prof Eugenio Oñate.

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• Eugenio Oñate’s Book Chapters

  Scope

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• Evaluation of the properties of Elevated Short Term Aged Asphalt Binder

  Scope

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• Evaluation of the properties of Elevated Short Term Aged Asphalt Binder

  Scope

  The rheological properties of Asphalt binder are dependent on the induced temperature during mixing, hauling and compaction. Different literatures and manuals recommend the short term aging property to be checked for period of 85 minutes but during construction on the site this time will not be attained and there is a probability of increase on this time.

  The objective of this study is to determine time-dependent aging property of 80/100 penetration grade bitumen. Four specimens of equal weight were extracted from the Penetration grade-80/100 bitumen. The first specimen checked for quality requirements. The other three specimens are aged using Rolling Thin Film Oven for 85, 115, and 145 minutes to simulate the delay during HMA production, hauling, and compaction. Test results indicated as the time of aging increased, penetration and ductility decreased, while, softening point, flash point, and mass loss increased, but a fire point remained constant. Further rheological study using AST and FST test indicate that there is property change on the stiffness and flow property of asphalt binder.

  Scope

  The rheological properties of Asphalt binder are dependent on the induced temperature during mixing, hauling and compaction. Different literatures and manuals recommend the short term aging property to be checked for period of 85 minutes but during construction on the site [...]

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• F. Salazar Self-Archive

  Scope

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• F. Salazar's Conference proceedings

  Scope

  Collection of Fernando Salazar's contributions to congresses, mainly in the fields of dam engineering, numerical methods and hydraulic structures.

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• Gaceta Técnica

  Scope

  La revista Gaceta Técnica es una publicación semestral y constituye un  espacio académico para la divulgación de trabajos científicos originales, de carácter interdisciplinario y multidisciplinario relacionados con el campo de la Ingeniería Civil, Urbanismo, Arquitectura, así como Enseñanza y Ciencias Básicas aplicadas en Ingeniería.
  Visión: Ser un espacio de divulgación académico y científico en el área de la ingeniería, arquitectura, urbanismo y ciencias aplicadas  en ambientes educativos, con altos niveles de calidad académica.
  Misión: Promover redes de intercambio y cooperación mediante la publicación de artículos científicos y temáticas  vinculadas con el área de  la ingeniería civil, arquitectura, urbanismo y otras disciplinas científicas.
  Objetivos:

  1. Consolidar un medio para la difusión de los trabajos de investigadores noveles y expertos.
  2. Promover la producción intelectual en las distintas áreas del saber científico que hacen vida en el decanato y en la universidad.
  3. Brindar a la comunidad científica la oportunidad de intercambio de experiencias exitosas en la construcción y promoción del conocimiento científico.
  4. Estimular la productividad académica de los docentes e investigadores.
  5. Fortalecer la creación y desarrollo de actividades científicas orientadas a mejorar la calidad de vida de la sociedad.

  Gaceta Técnica journal is published twice a year and is an academic for the dissemination of original scientific papers, interdisciplinary and multidisciplinary related of Civil Engineering, Urban Planning, Architecture and Basic Education and Applied Sciences in Engineering space.
  Vision: To be a space for academic and scientific disclosure in the field of engineering, architecture, urban planning and applied sciences in educational environments with high levels of academic quality
  Mission: To promote networks of exchange and cooperation through the publication of articles scientific and themes related to the area of civil engineering, architecture, urbanism and other scientific disciplines.
  Goals:

  1. Consolidate a means for disseminating the work of researchers’ novice and experts.
  2. Promote intellectual production in different areas of knowledge scientist
  3. Provide to the community scientific opportunity to exchange experiences successful in building and promoting scientific knowledge.
  4. Stimulate the productivity academic of teachers and researchers.
  5. Strengthen the creation and development of activities scientific aimed at improving the quality of life of society.

  Scope

  La revista Gaceta Técnica es una publicación semestral y constituye un  espacio académico para la divulgación de trabajos científicos originales, de carácter interdisciplinario y multidisciplinario relacionados con el campo [...]

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• Iber. More than 2D Hydraulic modelling

  Scope

  This collection aggluitinates some of the main publications of Iber (www.iberaula.com)

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• International workshop on Inkjet Technology and Beyond: Experiments, CFD, and Artificial Intelligence

  Scope

  This publication presents the collection of papers presented at the International workshop on Inkjet Technology and Beyond: Experiments, CFD, and Artificial Intelligence, held on July 11-12, 2024,  at CIMNE Institute, Technical University of Catalonia (UPC-BarcelonaTech). The venue gathered leading researchers and industry experts to showcase and disucuss the latest findings and innovations in the fields microfluidics applications, focusing on experimental insights, computational fluid dynamics (CFD), and artificial intelligence (AI) approaches.

  Scope

  This publication presents the collection of papers presented at the International workshop on Inkjet Technology and Beyond: Experiments, CFD, and Artificial Intelligence, [...]

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• IX International Conference on Particle-based Methods (Particles 2025)

  Scope

  The objectives of PARTICLES 2025 are to present and discuss the fundamental principles, the new trends and the cutting-edge features of state-of-the-art Particle Methods for solving problems in engineering and applied sciences.

  Particle Methods refer to those numerical methodologies using the concept of particles either to carry the information of the deforming continuum, such as Material Point Method (MPM), Smoothed Particle Hydrodynamics (SPH), Moving Particle Simulation (MPS), Particle Finite Element Method (PFEM), and Lattice-Boltzmann Method (LBM) or to represent computationally real physical particles, like in the Discrete Element Method (DEM) and Molecular Dynamics (MD).

  The conference is one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS) and a Special Interest Conference of the International Association for Computational Mechanics (IACM).

  Scope

  The objectives of PARTICLES 2025 are to present and discuss the fundamental principles, the new [...]

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• J. Irazábal's Conference proceedings

  Scope

  Collection of Joaquín Irazábal's contributions to congresses, mainly in the field of particle methods applied to civil engineering  problems.

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• J. Irazábal's DEM ballast works

  Scope

  Joaquín Irazábal's works about the numerical modelling of railway ballast and other granular materials using the discrete element method

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• Journal of Earth Challenges in Civil Engineering Strategies

  Scope

  Journal of Earth Challenges in Civil Engineering Strategies​ is a peer-reviewed publication that will contain original research articles in the field of Geotechnical Engineering & Engineering Geology related to Civil Engineering (scientific journal) as well as documents aiming to exchange and discus on novel and creative ideas on theoretical and experimental researches.

  Scope

  Journal of Earth Challenges in Civil Engineering Strategies​ is a peer-reviewed publication that will contain original research articles in the field of Geotechnical Engineering & Engineering Geology related to Civil Engineering (scientific journal) as well as documents [...]

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• Journal of Validation of Computational Models

  Scope

  The Journal of Validation of Computational Models aims to be a vehicle to disseminate experiments and illustrative analytical solutions that can be used for validation and verification of computational models in all areas of engineering and applied science.

  The journal welcomes contributions that present high-quality experiments with particular attention to the reproducibility of the results. The papers must have a detailed and complete description of the experimental set-up and should include dispersion or uncertainty assessment information. They should also be completed with the datasets of the presented results in standard editable format.

  Papers that focus on validation must involve assessment of computational models through comparison to experiments and should be accompanied by relevant measures of uncertainty from both sources. These papers should also include code verification, i.e. the assurance that code outputs converge to analytical solutions, particularly in terms of the rate of reduction of discretization errors.

  Scope

  The Journal of Validation of Computational Models aims to be a vehicle to disseminate experiments and illustrative analytical solutions that can be used for validation and verification of computational models in all areas of engineering and applied science.

  The journal [...]

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• Journal Papers

  Scope

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• Journal  of  Sustainable   Construction   Materials  and  Project   Management

  Scope

  Aim and Scope

  The Journal of Sustainable Construction Materials and Project Management (JSCMPM) acts as a leading international platform for advancing knowledge and innovation in the sustainable use of construction materials and the effective management of construction projects. It aims to promote evidence-based solutions to urgent challenges in resource efficiency, environmental stewardship, climate resilience, and equitable development in the built environment. By encouraging interdisciplinary research and practice, the journal supports the University’s research agenda and contributes to UN SDGs: SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible consumption and production), and SDG 13 (Climate Action). Through rigorous academic exchange and knowledge sharing, the journal seeks to advance sustainable development in the built environment and related fields.

  JSCMPM welcomes original research articles, technical notes, review papers, and case studies that present novel insights, systematic analyses, and practical applications in the following key areas of interest, including, but not limited to: 

  Sustainable and Alternative Construction Materials: Research on recycled aggregates, quarry dust, fly ash, calcined earthen resources, industrial by-products, innovative admixtures, and composite materials that reduce environmental impact and improve construction efficiency; including life-cycle assessment, durability studies, and circular economy practices.

  Construction Project Management: Advances in construction productivity, resource optimization, quality and safety management, sustainable procurement, risk and contract management, logistics, digital transformation (e.g., BIM, digital twins), and waste minimization in project delivery.

  Transportation Engineering: Studies addressing sustainable transportation infrastructure, encompassing innovative pavement and roadway materials, traffic flow analysis and modeling, smart and green mobility solutions, safety and logistics management, and other strategies that enhance efficiency, resilience, and environmental sustainability of transportation networks, particularly in developing and climate-vulnerable regions, in alignment with the SDGs.

  Infrastructure Systems and Applications: Sustainable approaches for roads, bridges, railways, ports, harbors, airports, and other critical infrastructure, integrating new materials and methods to improve resilience, climate adaptation, and low-carbon development.

  Policy, Economics, and Education for Sustainability: Insights on regulatory frameworks, financing models, incentives, educational initiatives, and capacity-building strategies that promote the adoption of sustainable materials and project management practices in the construction industry.

  By providing a platform for rigorous research and knowledge exchange across academia, industry, and government, JSCMPM supports evidence-based innovations that advance the construction and transportation sector’s contribution to sustainable development.

  How to Submit your Manuscript

  Under the Information section on the right, please follow the submission guidelines carefully. Manuscripts must be prepared in the IMRAD format and submitted as Microsoft Word (.docx) files, not as PDFs. Ensure that all tables and figures are fully editable and not inserted as images. In addition, authors are required to provide their ORCID ID, which uniquely identifies researchers and helps distinguish their scholarly contributions across publications, datasets, and institutional affiliations.

  Publication Frequency

  The Journal of Sustainable Construction Materials and Project Management (JSCMPM) is published biannually (twice a year), with issues released in:

  • June (First Issue)
  • December (Second Issue)

  Special issues may be organized on emerging themes and collaborative research projects with academic and industry partners.

  Copyright and Licensing

  The corresponding author must submit a duly signed Author Declaration and Publishing Agreement, affirming that the manuscript is original, has not been previously published or concurrently submitted elsewhere, and that all listed co-authors have reviewed and approved the submission. All submissions are subject to plagiarism screening using Turnitin or a similar similarity detection tool. The manuscript must adhere to the Journal’s plagiarism threshold of not more than fifteen percent (≤15%), excluding reference lists and standard academic phrases.

  Authors retain full copyright to their published work. The Journal of Sustainable Construction Materials and Project Management (JSCMPM) is granted the right of first publication, after which the article is distributed under the Journal’s open-access license:

  Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0)
  https://creativecommons.org/licenses/by-nc-sa/4.0/

  This license allows others to copy, distribute, display, perform, and adapt the work for non-commercial purposes, provided that:

  1. Proper attribution is given to the original authors and to JSCMPM as the original publisher; and
  2. Any adapted or derivative works are distributed under the same CC BY-NC-SA 4.0 license.

  All articles published in JSCMPM are freely and immediately accessible to the public upon publication. The Journal does not charge any submission fees or article processing charges (APCs). Publishing in JSCMPM is free for all authors.

  Publisher and Hosting Information

  Published by Cagayan State University, Carig Campus. The Journal of Sustainable Construction Materials and Project Management (JSCMPM) is hosted and disseminated online via the Scipedia publishing platform, ensuring broad, open access and global scholarly reach.

  Indexing and Abstracting Status

  The Journal of Sustainable Construction Materials and Project Management (JSCMPM) is currently undergoing the indexing process with several major academic databases. The editorial board is actively complying with the requirements of recognized abstracting and indexing services to enhance the journal’s visibility, scholarly reputation, and accessibility.

  Once approvals are secured, the journal will be included in relevant indexing platforms, ensuring that published articles are widely discoverable by researchers, academics, practitioners, and policymakers in the fields of Sustainable Construction Materials and Project Management (JSCMPM).

  Official updates will be released as soon as confirmations are received from the indexing services.

  In the meantime, the Editorial Board is pleased to invite the submission of manuscripts for peer review and online publication in the Journal of Sustainable Construction Materials and Project Management (JSCMPM).

  SCIPEDIA and Institutional Stewardship

  Maintained and supervised by Cagayan State University, Carig Campus, in partnership with SCIPEDIA, the Journal of Sustainable Construction Materials and Project Management (JSCMPM) upholds rigorous standards of quality and ethical publishing. This stewardship ensures academic integrity, alignment with international publishing protocols, and the open-access dissemination of high-quality, impactful research.

  Note to Readers

  For optimal readability and full appreciation of the figures, tables, and text layout used in this article, we highly recommend downloading and viewing the PDF version in double-column format. The double-column PDF layout offers clearer font scaling and a more organized academic presentation compared to the single-column PDF view.

  Office Address

  JSCMPM Editorial Team Main Office Address:

  Cagayan State University 

  RDE Office, Administration Building, Carig Campus, Tuguegarao City 3500, Philippines

  E-mail: RDE@csucarig.edu.ph  || emer.quezon@csucarig.edu.ph || fsdumlao@gmail.com  

  Telephone number: 0783952782 Loc 044 

   

  Principal Contact

   

  Emer T. Quezon, PhD, MSc.CE, M.ASCE 

  Editor-in-Chief  

  Campus Director for Research, Development & Extension

  Professor (Associate) of  Graduate School & College of Engineering & Architecture, Carig Campus, Cagayan State University, 3500 Tuguegarao City, Philippines. 

  E-mail: emer.quezon@csucarig.edu.ph || quezonet09@gmail.com

  Phone: +639451631743

   

  Journal Consultants and Technical Advisers

   

  Arthur G. Ibanez, Ph.D, ASEAN Engr.  

  JSCMPM consultant & Technical Adviser

  University Professor, OIC-University President, Cagayan State University, 3500 Tuguegarao City, Philippines. 

  E-mail: arthur_ibanez20@csu.edu.ph

   

  Florentina S. Dumlao, Ph.D  

  JSCMPM consultant & University Technical Adviser, Cagayan State University, 3500 Tuguegarao City, Philippines. 

  E-mail: florentinadumlao@csucarig.edu.ph || fsdumlao@gmail.com

   

  Prof. Roger P. Rumpon  

  JSCMPM consultant & Technical Adviser

  Campus Executive Officer, Carig Campus, Cagayan State University, 3500 Tuguegarao City, Philippines. 

  E-mail: rumpon.roger@csu.edu.ph

   

  Belen Garcia de Pablos, MSc, PhD 

  SciPedia & JSCMPM consultant, Technology Universidad Politecnica de Madrid

  Madrid, Spain  

   

  KSS Rakesh, Ph.D

  JSCMPM consultant & Technical Adviser

  Professor, International Institute of Cambodia University of Technology, Phnom Penh, Cambodia 

  E-mail: kssrakesh@iic.edu.kh

  Editorial Board Members and Peer Reviewers

  (1) JSCMPM Editorial Board Members and Peer Reviewers (Local Members)

  John Michael B. Casibang, MSc.CE, MST

  Editorial Board Member and Peer Reviewer

  Professor (Associate) of  Graduate School & College of Engineering & Architecture, Carig Campus, Cagayan State University, 3500 Tuguegarao City, Philippines. 

  E-mail: jmcasibang@csu.edu.ph

   

  Frances Lorane T. Calapini, MSc.CE, PhD-Cand.

  Editorial Board Member and Peer Reviewer

  Professor (Assistant) of  Graduate School & College of Engineering & Architecture, Carig Campus, Cagayan State University, 3500 Tuguegarao City, Philippines. 

  E-mail: frances_calapini@dlsu.edu.ph

   

  Ralph Angelo B. Estiller, MCRSDM 

  Editorial Board Member and Peer Reviewer

  Professor (Assistant), College of Engineering & Architecture, Carig Campus, Cagayan State University, 3500 Tuguegarao City, Philippines. 

  E-mail: ralphangeloestiller@csucarig.edu.ph

   

  Cyrus Kelly Macabangon, MSc 

  Editorial Board Member and Peer Reviewer

  Professor (Assistant), College of Engineering & Architecture, Carig Campus, Cagayan State University, 3500 Tuguegarao City, Philippines. 

  E-mail: cyrusmacabangon@csucarig.edu.ph

   

  Karl Louis S. Quezon, MSIT

  Editorial Board Member

  Campus Records Officer, Carig Campus, Cagayan State University, 3500 Tuguegarao City, Philippines

  E-mail: karllouie.quezon@csucarig.edu.ph

   

  (2) JSCMPM Editorial Board Members and Peer Reviewers  (International Members)

   

  Leevesh Kumar, Ph.D

  Editorial Board Member and Peer Reviewer

  Professor (Associate), Department of Civil Engineering, World University of Bangladesh, Dhaka, Bangladesh

  E-mail: leevesh.kumar@civil.wub.edu.bd  

   

  Alemayehu Gebissa Guta, Dr. Ing

  Editorial Board Member and Peer Reviewer 

  Professor, University of Rostock, Rostock, Germany

  E-mail: alemayehu.gebissa@uni-rostock.de

   

  Sitesh Kumar Singh, Ph.D

  Editorial Board Member and Peer Reviewer

  Professor (Assistant), Department of Civil Engineering, National University of Science and Technology, Muscat, Oman 

  E-mail: siteshsingh@nu.edu.om  

   

  Tesfaye Alemu, Ph.D

  Editorial Board Member and Peer Reviewer

  Professor (Associate), Head, Construction Quality & Technology Center of Excellence, Addis Ababa Science & Technology University, Ethiopia 

  E-mail: tesfaye.alemu@aastu.edu.et

   

  Abdin Bedada, Ph.D 

  Editorial Board Member and Peer Reviewer

  Professor (Assistant), Dean, School of Civil & Environmental Engineering, Hachalu Hundessa Campus, Ambo University, Ambo, Oromia, Region, Ethiopia  

  E-mail: abdin.bedada@ambou.edu.et   

   

  Biruk Tadele, Ph.D

  Editorial Board Member and Peer Reviewer

  Professor (Assistant), Department of Civil Engineering, Haramaya Institute of Technology, Haramaya University, Haramaya, Ethiopia 

  E-mail: biruk.tadele@aastu.edu.et  

  Scope

  Aim and Scope

  The Journal of Sustainable Construction Materials [...]

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• LEILSON JOAQUIM ARAUJO's personal collection

  Scope

  LEILSON JOAQUIM ARAUJO's personal collection

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• Modeling in Mechanics and Materials with Computational Methods and AI-Driven Innovations

  Scope

  Deadline Date: 01 September 2026

   

  The fields of mechanics and materials science are undergoing a profound transformation, driven by the convergence of high-fidelity computational methods and groundbreaking advances in artificial intelligence (AI). Accurately describing the mechanical behavior of materials is of great significance for their engineering applications. Traditional computational techniques, such as the Finite Element Method (FEM), Meshfree Methods (MM), Discrete Element Method (DEM), and Molecular Dynamics (MD), have long been the cornerstone for simulating complex physical phenomena. However, they often face significant challenges in dealing with multi-scale problems, inverse design, and extreme computational costs.

  With the rapid development of artificial intelligence (AI), advanced techniques such as neural networks are expected to bridge the gap in mechanical behavior in different scales, driving traditional material mechanics research from a "physics-driven" paradigm to a "data-physics hybrid-driven" paradigm. AI technologies not only accelerate the modeling and simulation of complex systems but also show great potential in uncovering underlying physical laws, predicting mechanical behaviors, and optimizing material structures. In particular, methods such as neural networks and graph learning provide novel approaches to address challenges in multi-physics coupling, spatiotemporal predictions, and nonlinear behavior characterization.

  Despite the widespread application of neural network technologies in material mechanics, numerous challenges remain. Many data-driven computational methods lack explicit constraints on physical laws such as constitutive relationships and energy conservation, leading to predictions that violate fundamental mechanical principles. Additionally, these methods suffer from strong data dependency, high costs of obtaining high-quality material data, poor interpretability, and limited generalization capabilities. Although physics-informed neural networks (PINNs) partially alleviate these issues by incorporating physical constraints, they still face challenges such as limited adaptability to complex scenarios and the absence of robust theoretical validation frameworks. These technical bottlenecks highlight the urgent need for breakthroughs in both theory and practice within the field of intelligent computation for material mechanics.

  This special issue aims to bring together cutting-edge research at the intersection of AI, computational mechanics, and materials science, addressing the core challenges of the current "data-physics hybrid-driven" paradigm. We encourage original research that overcomes the limitations of traditional computational mechanics models, enhances physical constraints, and improves the generalization and interpretability of AI models. Priority topics include:

  Exploring methods to enhance model transparency and interpretability, and establishing a quantitative evaluation framework for computational mechanics and AI-based mechanics models.

  Addressing the scarcity of high-quality material data by investigating small-sample learning methods and data augmentation strategies in AI mechanics.

  Leveraging generative AI technologies to design novel materials with specific microstructures, molecular configurations, or macroscopic structures based on target performance requirements, enabling AI-driven inverse material design.

  Integrating traditional physical simulations (e.g., finite element methods, meshfree methods) with AI surrogate models to construct efficient hybrid analysis frameworks for accelerating the solution of complex nonlinear dynamics problems in materials.

  We invite submissions from theoretical, computational, and applied research in material mechanics, computational science, and artificial intelligence, particularly those demonstrating methodological rigor, experimental validation, and engineering applicability. Topics of interest include, but are not limited to:

  Advanced Computational Methods: Material mechanics, Multi-scale and multi-physics modeling (such as FEM, MM, DEM, MD, etc.), phase-field modeling and fracture mechanics, contact mechanics and large deformation analysis, as well as uncertainty quantification and probabilistic modeling, and so on.

  Physics-informed AI algorithms: Advances in physics-informed neural networks for material mechanics and other methods embedding physical constraints.

  Automated scientific discovery: Applications of symbolic regression, tensor networks, and other methods for automatically discovering or identifying material constitutive relationships and physical laws.

  Intelligent microstructure characterization and generation: Graph neural networks for microstructure characterization and damage evolution, and generative models for virtual microstructure reconstruction and optimization.

  AI-enabled multiscale modeling: Constructing efficient surrogate models to replace computationally expensive microscale simulations by learning from high-fidelity simulation data and coupling information across scales.

  Intelligent multiphysics coupling: AI solvers and hybrid modeling strategies for complex coupled problems such as thermo-mechanical-chemical-electrical interactions.

  Data-driven mechanics models: Data-driven paradigms for crystal plasticity, phase-field models, and damage mechanics.

  AI technologies for additive manufacturing: Real-time simulation of 3D printing processes, defect prediction, and AI-based optimization of process parameters.

  Intelligent performance evaluation and health monitoring: AI-based predictions of material fatigue life, fracture behavior analysis, and intelligent evaluation of structural health.

  Inverse design of new structures and materials: Searching for optimal structures or material compositions to achieve given performance targets, such as odd elasticity theory, functionally graded materials, plate and shells, metamaterials mechanics.

  AI-driven digital twins: Developing AI surrogate models for real-time prediction, decision support, and performance optimization in material and structural systems.

  This Special Issue aims to gather these frontier studies to provide powerful computational tools for addressing major challenges in advanced materials, accelerating the innovation cycle from material mechanics to engineering application.

  Scope

  Deadline Date: 01 September 2026

   

  [...]

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• Monograph Series in Earthquake Engineering (CIMNE)

  Scope

  The collection gathers the monographs on earthquake engineering of the International Centre for Numerical Methods in Engineering (CIMNE).

  • 78
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• Monographs of the International Centre for Numerical Methods in Engineering (CIMNE)

  Scope

  The collection gathers the monographs on different research topics of the International Centre for Numerical Methods in Engineering (CIMNE).

  • 175
  • 27133
  • 5/5
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• Numerical Analysis of Axisymmetric Solids by the Finite Element Method Use in Concrete, Steel and Mixed Steel-Concrete Elements

  Scope

  The objective of the this paper is to present the numerical results of the computational implementation of a mathematical formulation based on the Finite Element Method (FEM) for the analysis of axisymmetric structures subjected to axisymmetric loads with materials in linear elastic regime. Fortran 90/95 (1) programming language was used to implement this formulation which allows obtaining stress, strain and displacement values along the axisymmetric structure. The implementations performed were checked by comparing the responses of the implemented computer program with results in the literature and/ or modelings carried out with the aid of ANSYS 17 software.

  Scope

  The objective of the this paper is to present the numerical results of the computational implementation of a mathematical formulation based on the Finite Element Method (FEM) for the analysis of axisymmetric structures subjected to axisymmetric loads with materials in linear elastic [...]

  • 1
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• Numerical investigation of perforated steel plate shear walls exposed to high temperature

  Scope

  To our knowledge, till now, the seismic performance of perforated steel plate shear walls under fire condition has not been evaluated and compared at different temperatures. In this paper, the perforated steel plate shear wall with infill plates with a thickness of 2.67, 5 and 7 mm and also with yield stresses of 165, 256 and 300 is modelled and their seismic performance in terms of stiffness, ultimate strength and ductility, before and after applying the load is compared under fire condition fire at temperatures of 458, 642 and 917 °C.

  Scope

  To our knowledge, till now, the seismic performance of perforated steel plate shear walls under fire condition has not been evaluated and compared at different temperatures. In this paper, the perforated steel plate shear wall with infill plates with a thickness of 2.67, 5 and [...]

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• Papers Repository of the International Centre for Numerical Methods in Engineering (CIMNE)

  Scope

  The Papers Repository of the International Centre for Numerical Methods in Engineering (CIMNE) is an online archive aimed at collecting, preserving, and disseminating digital copies of scientific papers published by researchers of the International Center for Numerical Methods in Engineering (CIMNE).

  The goal of the repository is to widely disseminate the results of the research performed at CIMNE, by sharing the preprints or postprints -depending on the article sharing policies of the original publisher- of the papers published in scientific journals by CIMNE's scientists.

  Scope

  The Papers Repository of the International Centre for Numerical Methods in Engineering (CIMNE) is an online archive aimed at collecting, preserving, and disseminating digital copies of scientific papers published by researchers of the International [...]

  • 2176
  • 24203
  • 5/5
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• Personal Repository of Francisco Zarate

  Scope

  Technical and scientific documents

  • 33
  • 1162
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• Presentaciones de la reunión de la red internacional de Aulas CIMNE 2017

  Scope

  Esta colección incluye las presentaciones realizadas en la reunión de la red internacional de Aulas CIMNE, celebrada el viernes 8 septiembre 2017 en Barcelona.

  • 2
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• Presentations and videos to 10th edition of the conference on Textile Composites and Inflatable Structures (STRUCTURAL MEMBRANES 2021)

  Scope

  The objectives of Structural Membranes 2021 are to collect and disseminate state-of-the-art research and technology for design, analysis, construction and maintenance of textile and inflatable structures. The conference will address both the theoretical bases for structural analysis and the numerical algorithms necessary for efficient and robust computer implementation. A significant part of the conference will be devoted to discuss advances in new textile composites for applications in membrane and inflatable structures in the building and construction sectors, as well as in innovative design, construction and maintenance procedures.

  Structural Membranes 2021 aims to be a forum for discussing recent progress and identifying future research directions in the field of textile composites and inflatable structures.

  Structural Membranes 2021 will be supported by the IASS. It will also be a Thematic Conference of the European Community on Computational Methods in Applied Sciences (ECCOMAS) and a Special Interest Conference of the International Association for Computational Mechanics (IACM).

  Conference organized by CIMNE Congress Bureau.

  Scope

  The objectives of Structural Membranes 2021 are to collect and disseminate state-of-the-art research and technology for design, analysis, construction [...]

  • 80
  • 1781
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• Presentations Repository of the International Centre for Numerical Methods in Engineering (CIMNE)

  Scope

  The Presentations Repository of the International Centre for Numerical Methods in Engineering (CIMNE) is an online archive aimed at collecting, preserving, and disseminating digital copies of presentations made by researchers of the International Center for Numerical Methods in Engineering (CIMNE).

  The goal of the repository is to widely disseminate the results of the research performed at CIMNE, by sharing those presentations made by CIMNE's scientists.

  Scope

  The Presentations Repository of the International Centre for Numerical Methods in Engineering (CIMNE) is an online archive aimed at collecting, preserving, and disseminating digital copies of presentations made by researchers of the [...]

  • 9
  • 503
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• Presentations to the XIV International Conference on Computational Plasticity (COMPLAS 2017)

  Scope

  The objectives of the XIV International Conference on Computational Plasticity, Fundamentals and Applications (COMPLAS 2017, http://congress.cimne.com/complas2017) are to address both the theoretical bases for the solution of plasticity problems and the numerical algorithms necessary for efficient and robust computer implementation. COMPLAS 2017 aims to act as a forum for practitioners in the field to discuss recent advances and identify future research directions.

  COMPLAS 2017 is one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS). It is also an International Association for Computational Mechanics (IACM) Special Interest Conference.

  COMPLAS conferences are organized by CIMNE Congress Bureau.

  The next edition of this series of conferences (COMPLAS 2019) will be held in Barcelona, Spain, 3-5 September 2019 (https://congress.cimne.com/complas2019)

  Scope

  The objectives of the XIV International Conference on Computational Plasticity, Fundamentals and Applications (COMPLAS 2017, http://congress.cimne.com/complas2017) are to address both the theoretical [...]

  • 8
  • 4687
  • 5/5
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• Ramon Ribó research documents

  Scope

  • 5
  • 1431
  • 5/5
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• Research Reports of the Aulas CIMNE Network

  Scope

  The collection gathers the Research Reports of the Aulas CIMNE Network.

  • 0
  • 0
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• Research Reports of the International Centre for Numerical Methods in Engineering (CIMNE)

  Scope

  The collection gathers the Research Reports of the International Centre for Numerical Methods in Engineering (CIMNE).

  • 302
  • 11925
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• Revista Internacional de Desastres Naturales, Accidentes e Infraestructura Civil

  Scope

  The scope of this publication covers engineering systems that provide support and help in the design of civil infrastructure, and to the natural disasters and accidents caused by human which may affect the infrastructure. The journal emphasizes contributions which address one or more of the areas identified in the title. The term infrastructure refers here to the physical facilities which allow moving or storing products, raw materials, water, solid waste, energy, information or people. This includes bridges, ports, canals, airports, railroads, systems of urban traffic, roads, lifelines, energy transportation, pipelines, dams, water treatment plants, tanks, silos, and others. Emphasis on natural disaster is placed on hurricanes, tornados, earthquakes, floods, drafts, fire, landslides, and others. The journal also deals with man-made accidents, such as design or construction errors, explosions, impacts with vehicles, and others.

  El alcance de esta publicación comprende los sistemas de ingeniería que dan apoyo y sirven para el diseño de la infraestructura civil, y a los desastres naturales y accidentes de origen humano que pueden afectar esa infraestructura. La revista publica contribuciones que se refieran a la conjunción de más de una de las áreas temáticas definidas en el título, o a una de ellas. El término infraestructura civil se usa aquí para designar al conjunto de instalaciones físicas que permiten movilizar o almacenar bienes, materias primas, agua, residuos, energía, información o personas. En general, se incluyen aquí puentes, puertos, canales, aeropuertos, ferrocarriles, sistemas de tránsito urbano, carreteras, líneas de comunicación y energía, tuberías, represas, plantas de tratamiento de aguas, tanques, silos, etc. El énfasis en desastres naturales está en el estudio de acciones de huracanes, tornados, terremotos, inundaciones, sequías, fuego, deslizamientos, y otros. Asimismo la revista publica temas relacionados con accidentes y eventos producidos por causas humanas, incluyendo fallas por diseño o construcción, colisiones con vehículos, explosiones, entre otras.
   

  Scope

  The scope of this publication covers engineering systems that provide support and help in the design of civil infrastructure, and to the natural disasters and accidents caused by human [...]

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• Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería

  Scope

  RIMNI - An International Journal of Numerical Methods for Calculation and Design in Engineering (Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería) is a peer-reviewed Open Access journal, founded in 1985. RIMNI publishes articles written in Spanish and English. The journal's scope includes Computational and Numerical Models of Engineering Problems, Development and Application of Numerical Methods, Advances in Software, Computer Design Innovations, Soft Computing, Machine Learning, Artificial Intelligence, etc. RIMNI is an essential source of information for scientists and engineers in numerical methods theory and applications. RIMNI contributes to the interdisciplinary exchange and thus shortens the distance between theoretical developments and practical applications.

  Indexing and Abstracting: SCIE (Web of Science): 2024 Impact Factor 0.4, JCR Q4; Scopus: 2024 CiteScore 0.6; DOAJ; Wikidata; Crossref; Title DOI; ROAD; ZDB; EZB; OpenAlex; SuDoc; FATCAT; The Keepers, etc.

  RIMNI is published by Scipedia, officially supported by International Centre for Numerical Methods in Engineering (CIMNE). RIMNI is in collaboration with Tech Science Press from July 2024.

  Scope

  RIMNI - An International Journal of Numerical Methods for Calculation and Design in Engineering (Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería) is a peer-reviewed Open Access journal, founded in 1985. RIMNI publishes [...]

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• RIMNI SPECIAL ISSUE - Advanced Approaches and Applications in Engineering

  Scope

  Deadline Date: 31 December 2025

   

  With the advancement of technology, the methods for measuring and analyzing engineering data are constantly being innovated and improved. This enables the engineering data to deliver greater value, allowing analysis results to more closely reflect real-world situations. Emerging technologies such as Artificial Intelligence (AI), Big Data, Cloud Computing, and Machine Learning (ML) have a wide range of applications in the practice of design, control, planning, decision-making, and management activities in engineering manufacturing and services. These technologies can help expand, automate, and enhance the collection and processing of engineering data, and improve engineers' ability to formulate strategies and value-added analysis and insights, thereby enhancing engineering performance and quality. We expect the submitted papers to present solutions and examples based on mathematical modelling and optimization, algorithms for application domains, software implementation, and other formal approaches or technologies. We are inviting submissions to this Special Issue entitled “Advanced Approaches and Applications in Engineering”. Both original research and reviews will be considered. The following subtopics are the particular interests of this special issue, including but not limited to:​

  • Artificial intelligence for data analytics in engineering;
  • Big data in engineering;
  • Machine learning/deep learning applications in engineering;
  • Statistics in engineering;
  • Performance evaluation in engineering;
  • System/process optimization in engineering;
  • Data analysis methods in engineering;
  • Uncertainty in engineering;
  • Decision-making in engineering.

  Scope

  Deadline Date: 31 December 2025

   

  With the advancement of technology, the methods for measuring and analyzing engineering [...]

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• RIMNI SPECIAL ISSUE - Advanced Numerical Methods for Fluids and Solids

  Scope

  Deadline Date: 15 December 2025

   

  Advanced numerical methods for fluids and solids are mathematical and algorithmic tools used to solve problems related to fluid mechanics and solid mechanics. These methods are used to model and simulate the behavior of fluids and solids in various such as airflow around an aircraft, wave motion in a solid medium, or the behavior of fluids in chemical reaction systems.

  There are many advanced numerical methods for fluids and solids, including finite difference methods, spectral methods, Monte Carlo methods, linear system solvers, and relaxation methods.

  Finite difference methods are based on the discretization of space and time to solve the equations of physics. They are widely used to solve problems in fluid mechanics and solid mechanics.

  Spectral methods are based on the representation of data as Fourier series or Legendre series. They are used to solve problems in fluid mechanics and solid mechanics in specific domains, such as airflow around an aircraft.

  Monte Carlo methods are based on random sampling to solve problems in fluid mechanics and solid mechanics. They are used to solve complex problems such as the behavior of fluids in chemical reaction systems.

  Linear system solvers are based on algorithms to solve systems of linear equations. They are used to solve problems in fluid mechanics and solid mechanics.

  Relaxation methods are based on algorithms to solve systems of nonlinear equations. They are used to solve problems in fluid mechanics and solid mechanics.

  In conclusion, advanced numerical methods for fluids and solids are important tools for solving complex problems in fluid mechanics and solid mechanics. They are widely used in various industries, such as aerospace, energy, chemistry, and medicine.

  Scope

  Deadline Date: 15 December 2025

   

  Advanced numerical methods for fluids and solids are mathematical and algorithmic tools used to solve problems [...]

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• RIMNI SPECIAL ISSUE - Advances in Fracture Mechanics and Fatigue Analysis of Materials and Structures

  Scope

  Deadline Date: 30 June 2026

  This Special Issue focuses on state-of-the-art research in the field of fracture mechanics and fatigue of materials and structures. The increasing demand for safe, reliable, and long-lasting engineering components has made understanding the fatigue and fracture behavior of materials a critical topic in mechanical design. This issue welcomes contributions that cover both theoretical and applied aspects, including experimental studies, analytical models, numerical simulations (such as FEM and XFEM), and life prediction methodologies. Special attention is given to innovations in damage detection, crack growth modeling, multiscale and multiphysics approaches, and the behavior of advanced materials such as composites, shape memory alloys, and additive-manufactured components. The aim is to foster interdisciplinary collaboration and promote new insights that improve structural integrity and durability.

  Scope

  Deadline Date: 30 June 2026

  This [...]

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• RIMNI SPECIAL ISSUE - Advances in Fuzzy Information Processing and Applications

  Scope

  Deadline Date: 30 June 2026

   

  In the real world, information is not always black-and-white and precise without a doubt. A great deal of human knowledge, perception and decision-making is built on "fuzziness". For instance, concepts such as "the temperature is very high", "the speed is very fast", and "this person is very young" have gradual and unclear boundaries. Traditional binary logic seems inadequate when dealing with this kind of information. Fuzzy information processing was born precisely to address this challenge. Its core theoretical basis is the theory of fuzzy sets. Unlike in classical set theory, where an element either completely belongs to or completely does not belong to a set, fuzzy sets describe the degree to which an element belongs to a certain set through a membership function, and this value varies continuously between 0 and 1. This enables us to quantify and manipulate these fuzzy concepts in mathematical language. The significance of fuzzy information processing lies in the effective modeling of uncertainties, serving as a bridge for human-computer interaction, enhancing the control and decision-making capabilities of complex systems, and promoting the development of intelligent computing.

  This special issue of RIMNI aims to showcase the latest research progress, innovative methods, and cutting-edge applications in the field of fuzzy information processing. We hope to bring together the wisdom of researchers around the world to explore how to utilize fuzzy theory to solve increasingly complex decision-making, control, and data analysis problems in the real world. The special issue will focus on the integration of fuzzy theory with other advanced computing paradigms, as well as its unique value in addressing the challenges of big data, artificial intelligence, and complex systems.

  This special issue welcomes high-quality original research or in-depth reviews covering theories, methods, algorithms, and applications. We encourage contributors to explore the boundaries of fuzzy information processing and apply it to emerging scientific and technological fields. The specific themes include, but are not limited to, the following topics:

  New fuzzy sets and their extended theories

  A new architecture of fuzzy logic and reasoning systems

  Fuzzy information granulation and word computation

  Fuzzy clustering and classification algorithms

  Fuzzy regression analysis and prediction model

  Fuzzy optimization and decision-making methods

  Fuzzy feature selection and dimensionality reduction techniques

  Fuzzy data mining and knowledge discovery

  Fuzzy affective computing

  Fuzzy image and video processing

  Fuzzy neural networks and their deep learning variants

  The integration of fuzzy systems with rough sets and evidence theory

  Fuzzy reinforcement learning

  Fuzzy product design

  Interpretable artificial intelligence based on fuzzy rules

   

  Scope

  Deadline Date: 30 June 2026

  [...]

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• RIMNI SPECIAL ISSUE - Advances in Numerical Methods and Computational Techniques Across Engineering Disciplines

  Scope

  Deadline Date: 31 August 2025

   

   

  Introduction: Numerical methods are foundational to solving complex problems across diverse fields of engineering and applied sciences. From structural analysis and fluid mechanics to electrical systems and biomedical engineering, numerical methods provide the computational tools necessary for simulation, optimization, and design. As engineering challenges grow in complexity, the need for innovative numerical techniques and computational strategies becomes increasingly important. This Special Issue aims to bring together cutting-edge research that advances the theoretical development and practical application of numerical methods across various disciplines, fostering interdisciplinary collaboration and knowledge sharing.

   

  Aim and Scope: The aim of this Special Issue is to publish high-quality research papers that address the development, improvement, and application of numerical methods in different branches of engineering and science. Contributions that explore new algorithms, computational models, and the use of advanced software for solving real-world problems are encouraged. The issue will serve as a platform to disseminate innovations that can be applied in multiple fields.

   

  Suggested Themes:

  • Development of novel numerical algorithms
  • Multidisciplinary applications of numerical methods
  • Numerical methods in machine learning
  • Advances in computational fluid dynamics (CFD)
  • Numerical methods in mechanical, materials and structural engineering
  • Optimization techniques in engineering
  • High-performance computing in numerical simulations
  • Numerical methods in bioengineering and environmental science
  • High-performance computing in numerical simulations
  • Numerical methods, fuzzy systems and aggregation operators in decision-making for industrial management, business, and economics

  Scope

  Deadline Date: 31 August 2025

   

   

  Introduction: Numerical methods are foundational to solving complex problems across diverse fields of engineering and applied sciences. [...]

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• RIMNI SPECIAL ISSUE - Advances in Special Functions: Applications in Mathematical Physics and Computational Methods

  Scope

  Deadline Date: 30 September 2025

   

  The study of modern engineering and physical sciences increasingly demands a deep understanding of applied mathematics, especially special functions. These functions play a key role in fields such as acoustics, thermodynamics, electromagnetics, and optics, helping to express both approximate and exact analytical solutions to complex problems. This, in turn, provides clearer insights into the fundamental properties and mechanisms involved.

  This Special Issue aims to explore the use of classical and higher-order special functions in addressing advanced challenges in mathematical physics. These challenges may be defined by specific symmetries, such as rectangular, cylindrical, or spherical, or by unconventional models. We also encourage the study of new special functions, with a focus on their governing differential equations, recurrence relations, and the development of efficient computational algorithms, such as those utilizing uniform asymptotic expansions for small and large parameters.

  We invite you to contribute review articles and original research papers that highlight recent progress in the theory and applications of special functions.

  Scope

  Deadline Date: 30 September 2025

   

  The study of modern engineering and physical sciences increasingly demands a deep understanding of applied mathematics, [...]

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• RIMNI SPECIAL ISSUE - AI and Machine Learning for Engineering, Software Systems, and Real-World Applications Methods, Theories, Techniques and Evaluation

  Scope

  Deadline Date: 30 January 2026

   

  Introduction and Background

  Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized problem-solving across disciplines by offering advanced tools for automation, prediction, optimization, and decision support. In engineering and software development, AI-driven approaches are reshaping how we design, simulate, and validate complex systems. Simultaneously, the adoption of AI in fields like education, healthcare, finance, and cybersecurity underscores its broad social impact and growing necessity for responsible deployment.

   

  As AI systems become increasingly embedded in critical infrastructures and workflows, challenges around interpretability, reliability, adaptability, and human trust become central to their successful application. The integration of AI into numerical methods, computational models, and software systems calls for new research that bridges theoretical development with practical needs - precisely the mission of RIMNI.

  Aim and Scope of the Special Issue

  This Special Issue aims to bring together cutting-edge research on the development, analysis, and application of AI and ML techniques across both traditional engineering domains and emerging interdisciplinary contexts. We particularly encourage submissions that highlight numerical methods, computational modeling, and software innovations enriched by AI.

   

  In line with RIMNI's mission, the issue seeks to:

  Explore how AI and ML are advancing engineering, and simulation.

  Showcase AI applications in software engineering, especially those involving automation, defect detection, and predictive modeling etc.

  Present cross-domain applications of AI (e.g., in education, health, security) where computational rigor and system reliability are critical

  Address evaluation, ethics, and human-centered design of AI tools, particularly in high-stakes or regulated environments

  Suggested Themes

  We invite original research articles, reviews, and case studies on topics including (but not limited to):

  AI-enhanced numerical methods for simulation, modeling, and optimization.

  Integration of ML into engineering, software and mathematics.

  Data-driven modeling and soft computing for system analysis and control.

  AI for software analytics, automated testing, and intelligent development.

  NLP and ML applications in software engineering and requirements processing.

  AI for healthcare diagnostics, and educational personalization,

  Explainability, robustness, and fairness in AI-driven systems.

  Human-AI interaction and cognitive design in engineering and decision-making tools.

  Numerical rigor with AI adaptability.

  Evaluation frameworks and performance benchmarks for AI in engineering and social domains.

  ……

  RIMNI-The journal's scope includes Computational and Numerical Models of Engineering Problems, Development and Application of Numerical Methods, Advances in Software, Computer Design Innovations, Soft Computing, Machine Learning, Artificial Intelligence, etc. 

   

  Scope

  Deadline Date: 30 January 2026

   

  Introduction and Background

  Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized [...]

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• RIMNI SPECIAL ISSUE - AI-Aided Fault Diagnosis and Condition Monitoring for Power Equipment

  Scope

  Deadline Date: 31 May 2026

   

  The surging integration of new energy power systems, driven by global decarbonization, introduces complex operational dynamics—high variability, multi-source data interdependencies, and escalating equipment complexity - posing critical challenges to traditional diagnostic paradigms. Artificial Intelligence (AI), particularly deep learning, image recognition, and hybrid algorithms, has emerged as a cornerstone for intelligent equipment health management, offering unprecedented capabilities in extracting latent patterns from heterogeneous data (sensors, images, vibration signals) and enabling proactive decision-making. This Special Issue solicits original contributions leveraging AI to address key technical gaps in new energy equipment health management, focusing on the following directions: (1) Real-time identification of incipient anomalies (e.g., insulation degradation in transformers, microcracks in PV modules) under variable loads and environmental conditions; (2) High-precision localization and classification of faults (e.g., gear box faults in wind turbines, CT saturation in secondary devices) via multi-modal AI models; (3) Data-driven degradation modeling for remaining useful life (RUL) estimation, integrating physics-informed learning and transfer learning; (4) Proactive risk assessment using AI-driven early warning systems to prevent cascading failures; (5) Quantitative assessment of equipment health indices (e.g., solar panel efficiency, circuit breaker contact wear) with explainable AI; (6) Scalable AI frameworks for interoperable health management across diverse equipment (e.g., coupling solar inverters with grid switches). We prioritize studies that bridge AI algorithm innovation with domain-specific engineering needs, emphasizing real-world validation and scalability. Contributions should advance both theoretical rigor (e.g., model generalizability, uncertainty quantification) and practical utility. By fostering interdisciplinary dialogue, this issue aims to accelerate the development of AI-empowered, resilient new energy power systems, paving the way for a sustainable energy future.

  Scope

  Deadline Date: 31 May 2026

   

  The surging integration of new energy power [...]

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• RIMNI SPECIAL ISSUE - AI-Based Modeling in Heat Transfer Systems

  Scope

  Deadline Date: 31 December 2025

   

  The growing complexity of thermal systems in aerospace, energy conversion, and advanced manufacturing demands a paradigm shift beyond traditional numerical methods. While computational fluid dynamics (CFD) and finite element analysis (FEA) have been cornerstones of heat transfer modeling, challenges persist in handling nonlinearities, multi-phase interactions, and real-time optimizationareas where artificial intelligence (AI) and machine learning (ML) offer transformative potential.

  This Special Issue of RIMNI seeks to compile pioneering research at the intersection of AI and heat transfer, addressing critical gaps between theoretical ML advancements and their engineering applications. We highlight computational strategies where AI augments or replaces conventional methods to achieve:

  Higher accuracy in turbulent/transitional flow predictions,

  Reduced computational cost for multi-scale problems,

  Uncertainty quantification in complex boundary conditions,

  Autonomous optimization of thermal systems (e.g., heat exchangers, electronic cooling).

  Key Focus Areas:

  AI-Enhanced Modeling:

  Neural networks for conjugate heat transfer,

  Hyperparameter optimization in thermal surrogate models.

  Hybrid Numerical-AI Frameworks:

  Coupling CFD with reinforcement learning for active flow control,

  Generative adversarial networks (GANs) for synthetic thermal dataset generation.

  Industrial Applications:

  Digital twins of power plant cooling systems,

  AI-driven topology optimization for additive manufacturing heat sinks.

  Fundamental Advances:

  Interpretable ML for uncovering hidden heat transfer correlations,

  Transfer learning across dissimilar thermal systems.

  This issue aims to establish best practices for integrating AI into heat transfer workflows while critically examining limitations (e.g., data scarcity, overfitting risks). Contributions should demonstrate rigorous validation against experimental or high-fidelity numerical benchmarks.

  Scope

  Deadline Date: 31 December 2025

   

  The growing complexity of thermal systems in aerospace, energy [...]

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• RIMNI SPECIAL ISSUE - Algorithm Development for Deep Learning Models in Industrial Predictive Maintenance Systems

  Scope

  Deadline Date: 31 March 2026

   

  The integration of deep learning algorithms into predictive maintenance systems represents a significant advancement in the application of numerical methods for engineering design and decision-making. As industries increasingly adopt data-driven solutions to manage complex equipment and production processes, predictive maintenance has become a vital strategy for minimizing unexpected downtimes, reducing maintenance costs, and extending asset lifespans.

  This research area is critically important in the context of modern engineering, where large-scale sensor data, real-time monitoring, and intelligent analysis converge to form the foundation of Industry 4.0. Deep learning models—especially those capable of handling time series data and anomaly detection—enable engineers to forecast failures and optimize repair schedules with high accuracy. These models leverage numerical techniques rooted in statistical learning, neural network training, and signal processing to extract actionable insights from complex, multi-dimensional datasets.

  Given the growing demand for computationally efficient and scalable solutions, the development and validation of specialized algorithms for industrial predictive maintenance are of high relevance. This special issue seeks to highlight novel methodologies and practical implementations that bridge the gap between artificial intelligence, numerical modeling, and engineering maintenance systems—offering both theoretical advancements and real-world applications.

  The primary aim of this Special Issue is to explore and disseminate recent developments in algorithm design for deep learning models applied to industrial predictive maintenance systems. As industries transition toward intelligent and data-driven operations, the need for robust computational approaches to forecast equipment failures has become increasingly critical. This Special Issue focuses on research that bridges deep learning methodologies with practical engineering applications, particularly in the optimization of system reliability, efficiency, and cost-effectiveness.

  The scope encompasses innovative algorithmic strategies that utilize numerical and computational techniques to analyze sensor data, model degradation patterns, and predict faults within complex industrial environments. Topics of interest include the theoretical development of deep learning models, advanced signal processing for time series data, and the integration of industrial internet of things (IIoT) technologies to enable real-time predictive maintenance. The Special Issue also encourages submissions that provide comparative analysis of algorithms, discuss practical deployment challenges, or demonstrate real-world implementations in manufacturing and engineering systems.

  By uniting contributions from academia and industry, this Special Issue aims to provide a comprehensive perspective on how deep learning and numerical methods can jointly enhance predictive maintenance, thus contributing to the advancement of smart, sustainable, and efficient industrial systems.

  Suggested themes:

  Predictive maintenance for induction motors using deep learning and machine learning techniques.

  Industrial Internet of Things predictive maintenance facilitated by deep learning.

  Industrial predictive maintenance using machine learning.

  Predictive maintenance using machine learning for industry-wide sustainable smart manufacturing.

  A systematic overview of the literature on machine learning techniques used in predictive maintenance.

  Methods based on data that are used to estimate industrial equipment repair.

  An analysis of cutting-edge machine learning algorithms for predictive maintenance in comparison.

  Industrial production line predictive maintenance system.

  Predictive maintenance using machine learning in the manufacturing sector.

  Applications of data-driven predictive maintenance for temporal convolutional networks in industrial systems.

  A study of unsupervised machine learning techniques for predictive maintenance's early fault identification.

  Survey of future maintenance defect detection methods.

  Scope

  Deadline Date: 31 March 2026

   

  The integration of deep learning algorithms into predictive maintenance systems represents a significant advancement [...]

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• RIMNI SPECIAL ISSUE - Analytical Iterative and Transform Techniques for Solving Physical Problems

  Scope

  Deadline Date: 30 June 2026

   

  Analytical solutions play an important role and provide a deeper understanding of the physical solutions of differential equations. Although exact analytical solutions in the closed form functions are very rare, iterative analytical solutions in the form of series and/or transform techniques have proved to be effective in constructing semi-analytical solutions. Since they are complex in nature, these techniques are usually combined with various numerical techniques also. The aim of the special issue is to bring together scientist using common semi- analytical techniques together, to collect high-quality work and provide a dissemination of recent results on the topic.
  Perturbation techniques may be considered the oldest and common series type solutions. The limitations of the technique to small parameters lead to many iterative analytical techniques recently. Some of the techniques to avoid such restrictions are the Perturbation Iteration Method, Iteration Perturbation Technique, Homotopy Perturbation Method, Homotopy Analysis Method, Variational Iteration Method, Differential Transform Method, Adomian Decomposition Method, Optimal Homotopy Asymptotic Method, Taylor Matrix Method etc. Variants of the perturbation techniques which do not require small parameter assumptions are also welcome. Solutions of physical problems in any of the iterative analytical methods are within the scope of this special issue. 
  Another important unified approach to solve differential equations is the Lie Group theory and its restricted form similarity transformations. Usually, the equations are first transformed into a solvable form via the symmetries of the equations. Since the resulting equations are also complex in nature, resort to numerical techniques may be inevitable. Employment of such techniques in search of solutions to the physical problems is welcome. 
  Solutions of differential equations that do not stem from physical applications are discouraged. Also purely numerical solution techniques without applying some form of analytical techniques are not within the scope of this special issue. As mentioned above, a combination of analytical and numerical techniques in search of solutions of physical problems are within the scope of this special issue.  

  Scope

  Deadline Date: 30 June 2026

  [...]

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• RIMNI SPECIAL ISSUE - Application of Artificial Intelligence in Geomaterials

  Scope

  Deadline Date: 01 September 2026

   

   The geomaterial is a complicated natural geological body whose mechanical properties and internal structure are very complex. And most of the mechanical problems for the geomaterials are the coupling of multiple fields and multiple phases. The unsafety of geomaterials will bring serious engineering disasters, such as landslides and surface subsidence, which can not be solved well by the traditional methods, including the theoretical method, numerical method, and experimental method. To study the geomaterials, with the development of artificial intelligence (AI), the application of AI methods has become widespread, and it has become a hot research topic currently. The present Special Issue is intended for the presentation of new applications of AI methods in the field of geomaterials. The interest includes, but is not limited to, the applications of AI methods for rock, soil, and other geomaterials, as well as the applications of AI methods in geomechanics, etc.

   

  This Special Issue will publish high-quality, original research papers in the overlapping fields of:

   

  - Applications of artificial neural networks;

  - Applications of deep learning methods;

  - Applications of swarm intelligence;

  - Applications of Evolutionary Algorithms;

  - Applications of big data analysis;

  - Applications of biological computation;

  - Applications of nature-inspired computation;

  - Applications of support vector machine, support vector regression, etc..

  Others.

  Scope

  Deadline Date: 01 September 2026

   

   The geomaterial is a complicated natural geological body whose mechanical properties and internal structure [...]

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• RIMNI SPECIAL ISSUE - Artificial Intelligence Controlled Renewable Source Powered Converters for Grid Integrated Distribution Network

  Scope

  Deadline Date: 30 April 2026

   

  The increasing global demand for renewable energy sources like solar and wind power, coupled with the widespread adoption of power electronic devices, has led to power quality issues. Integrating renewable sources into the grid can alleviate the burden on power converters. This special issue of RIMNI - International Journal of Numerical Methods for Calculation and Design in Engineering aims to showcase innovative research on the optimal design, numerical modelling, and analysis of renewable source-integrated distribution systems that utilize Artificial Intelligence (AI) control techniques to enhance power quality, effective power management, performance, and cost-effectiveness.

  We invite review papers and original research contributions that focus on innovative algorithms, advanced controllers, AI-based optimization, and numerical modelling and design of:

  • Photovoltaic (PV) systems
  • Wind power systems (WPS)
  • Multilevel converters
  • Relevant topics include, but are not limited to:
  • Numerical modelling and design of multilevel converters for power quality improvement
  • Optimal power management strategies
  • AI-based deep learning techniques for control applications
  • Advanced simulation techniques for integrating hybrid renewable energy systems
  • Optimization of hybrid control systems that combine conventional and AI methodologies
  • Integration of AI algorithms in real-time control systems
  • Renewable energy systems and smart grids
  • AI-based tuning of fractional-order controllers

  Scope

  Deadline Date: 30 April 2026

   

  The increasing global demand for renewable energy sources like solar and wind power, coupled with the widespread adoption [...]

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• RIMNI SPECIAL ISSUE - Artificial Intelligence for Empowering UAV Communications in the Low-Altitude Economy

  Scope

  Deadline Date: 01 December 2026

   

  The burgeoning low-altitude economy is unlocking new frontiers for economic and technological innovation. UAVs, as a critical infrastructure, are at the heart of this revolution, enabling applications from smart logistics to urban air mobility. The reliability of these services is fundamentally dependent on robust, efficient, and intelligent communication networks. This creates an urgent need for advanced algorithms to solve complex challenges in real-time trajectory optimization, dynamic resource allocation, and large-scale swarm coordination under uncertain environments.

  This Special Issue aims to compile the latest breakthroughs in applying Artificial Intelligence (AI) to address these critical challenges in UAV-assisted communications. It will highlight how AI methodologies—including Evolutionary Computation, Deep Reinforcement Learning, Federated Learning, and other machine learning paradigms—provide innovative, scalable, and self-adaptive solutions. The focus is on leveraging AI's powerful learning and optimization capabilities to build the cognitive and decision-making core for next-generation autonomous UAV networks, thereby accelerating the sustainable growth of the low-altitude economy.

  1. AI-driven 3D path planning and collision avoidance in dense airspace.
  2. Deep reinforcement learning for dynamic spectrum management and interference mitigation.
  3. Federated and distributed learning for privacy-preserving and collaborative swarm intelligence.
  4. AI-based solutions for robust network operation under uncertainties and security threats.
  5. Multi-agent AI systems for heterogeneous UAV swarm coordination and control.

  Scope

  Deadline Date: 01 December 2026

   

  The burgeoning low-altitude economy is unlocking new frontiers for economic and technological innovation. UAVs, as a critical infrastructure, are at the heart of this [...]

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• RIMNI SPECIAL ISSUE - Artificial Intelligence in Engineering Simulation and Numerical Analysis for Industry 4.0

  Scope

  Deadline Date: 30 April 2026

   

  1) The issue introduction includes the background and the importance of this research area.

  The fusion of Artificial Intelligence (AI) with engineering simulation and numerical analysis is transforming the industrial landscape, forming a key pillar of Industry 4.0. Traditional numerical methods such as finite element analysis (FEA), computational fluid dynamics (CFD), and structural modeling are increasingly being enhanced through AI techniques—ranging from machine learning and deep neural networks to reinforcement learning and computer vision. This integration offers significant advantages: accelerated simulations, improved predictive accuracy, and reduced computational cost.

  AI-driven models allow for automated parameter optimization, adaptive mesh refinement, and real-time predictive simulations, making engineering processes smarter, faster, and more resilient. In industrial applications, these AI-powered tools enable better product design, lower material waste, and reduced time-to-market. Additionally, when integrated with cyber-physical systems and digital twins, they provide continuous monitoring and predictive maintenance capabilities, minimizing downtime and enhancing reliability.

  Despite these advancements, critical challenges remain. High computational complexity, lack of standardized integration frameworks, and concerns around interpretability and safety in mission-critical applications limit wider adoption. Addressing these issues is essential for realizing the full potential of AI in engineering.

  This special issue aims to explore cutting-edge research and developments in AI-augmented numerical methods, showcasing how they can unlock the next level of intelligent, data-driven engineering for Industry 4.0.

  2) The aim and scope of the Special Issue shall be highlighted.

  The aim of this Special Issue is to investigate and promote the integration of Artificial Intelligence (AI) with numerical analysis and engineering simulation in support of Industry 4.0 applications. As industries move toward smart, autonomous, and data-driven operations, AI offers powerful tools to enhance traditional simulation methods such as finite element analysis (FEA), computational fluid dynamics (CFD), and structural modeling. This issue seeks to highlight innovative AI methodologies—including machine learning, deep learning, reinforcement learning, and evolutionary computing—that improve model accuracy, reduce computational time, and enable real-time predictive simulations. The scope encompasses AI-driven simulation techniques for manufacturing, optimization, intelligent system design, digital twins, uncertainty quantification, and cyber-physical integration. By bridging theory and practice, the Special Issue aims to provide a forum for researchers and industry professionals to share advanced techniques and applications that redefine computational engineering for the next generation of intelligent, adaptive industrial systems.

  3) Suggested themes shall be listed.

  Evolutionary Algorithms in Engineering Simulation and Optimization for Industry 4.0

  Quantum-Inspired AI Techniques for Numerical Computation and Engineering Analysis

  Fuzzy Logic Systems for Adaptive Numerical Modeling in Engineering Applications

  Generative Adversarial Networks for Engineering Simulation and Data-Driven Numerical Analysis

  Bayesian Learning Models for Uncertainty Quantification in Numerical Methods and Simulation

  Physics-Informed Neural Networks for Engineering Simulation and Computational Analysis

  Swarm Intelligence Approaches to Numerical Optimization in Smart Manufacturing Systems

  Autonomous Multi-Agent Systems for Engineering Simulation and Predictive Numerical Analysis

  High-Performance Computing with AI for Large-Scale Engineering Simulations

  Probabilistic Graph Models for Numerical Analysis and Computational Engineering in Industry 4.0

  Metaheuristic AI Algorithms for Engineering Simulation and Complex Numerical Methods

  Cognitive Computing for Intelligent Numerical Modeling and Engineering Simulation in Industry 4.0

  Scope

  Deadline Date: 30 April 2026

  [...]

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• RIMNI SPECIAL ISSUE - Artificial Intelligence in Healthcare: Current Approaches in Disease Detection and Classification

  Scope

  Submission Deadline: 31 May 2025

  This special issue delves into the transformative impact of artificial intelligence (AI) and machine learning techniques on disease detection and classification in healthcare. Recent advancements have led to significant improvements in diagnostic accuracy and efficiency, particularly through applications involving various biomedical signals and imaging modalities. These include electroencephalograms (EEG), electromyograms (EMG), electrocardiograms (ECG), heart rate (HR) signals, computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and other medical images or videos. By leveraging these rich data sources, AI models can extract meaningful patterns and biomarkers essential for early detection and precise classification of a wide range of diseases.

  The collected works in this issue focus on the development of deep learning algorithms and innovative AI methodologies tailored to process and analyze these complex biomedical data. A key emphasis is placed on explainable artificial intelligence (XAI) techniques, which provide insights into how machine learning models perform classification tasks. XAI enhances transparency and interpretability, addressing critical concerns related to clinical trust and adoption of AI systems.

  We aim to showcase smart health applications that incorporate XAI-based next-generation methods, bridging the gap between sophisticated AI technologies and practical clinical implementation. By exploring the integration of these advanced tools into healthcare practices, the articles seek to optimize diagnostic workflows, improve patient outcomes, and contribute to the advancement of personalized medicine. This compilation serves as a comprehensive resource for clinicians, researchers, and practitioners interested in the latest AI-driven solutions for disease detection and classification.

  Scope

  Submission Deadline: 31 May 2025

  This special issue delves into the transformative impact of artificial intelligence (AI) and machine learning techniques on disease detection and classification in healthcare. Recent [...]

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• RIMNI SPECIAL ISSUE - Beyond Numerics: Analytical Insights into Nonlinear Electrical Phenomena

  Scope

  Deadline Date: 31 March 2026

   

  This special issue aims to bring together original research and review papers focused on analytical modeling and closed-form solutions to nonlinear problems arising in various areas of electrical engineering. With the increasing complexity of modern systems, there is a growing demand for precise, insightful, and computation-efficient models that go beyond purely numerical approaches.
  Topics of interest include, but are not limited to:
      • Closed-form expressions for nonlinear circuit and system behavior
      • Application of the Lambert W function and related transcendental equations (e.g., Tsallis-Lambert equation)
      • Analytical treatment of semiconductor devices, power electronic converters, and electromagnetic systems
      • Symbolic computation in electrical modeling
      • Novel techniques for solving nonlinear differential and integral equations in electrotechnical applications
      • Hybrid analytical-numerical approaches with clear physical interpretation
  This issue welcomes contributions from both theoretical and applied perspectives, and encourages authors to emphasize the interpretability, efficiency, and generality of the proposed analytical frameworks.

  Scope

  Deadline Date: 31 March 2026

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• RIMNI SPECIAL ISSUE - Computational Frontiers in Multiphysics Simulation and AI-Driven Design for Aerospace Systems

  Scope

  Deadline Date: 31 July 2026

  The design and development of next-generation aerospace systems require a seamless integration of advanced numerical methods, high-fidelity simulations, and artificial intelligence (AI)-driven approaches. High-speed aerospace vehicles, including hypersonic aircraft, reusable launch systems, and space exploration platforms, operate under extreme conditions where complex interactions among aerodynamics, propulsion, structures, and thermal environments must be accurately captured. Traditional modeling techniques, while powerful, often face limitations in handling the coupled physics, large-scale computations, and real-time predictive capabilities demanded by these applications.

  This special issue aims to provide a platform for disseminating pioneering research that advances the computational frontiers of aerospace science and engineering. Topics include high-speed aerodynamics and flow control, modeling of propulsion and combustion systems, multiphysics and multiscale simulations, aero-thermo-structural coupling, and re-entry aerothermodynamics. Contributions that leverage AI, machine learning, and data-driven methodologies such as surrogate modeling, reduced-order models, physics-informed neural networks, and digital twins are particularly encouraged, as they hold the potential to accelerate design cycles, improve predictive accuracy, and enable optimization at unprecedented scales.

  Scope

  Deadline Date: 31 July 2026

  The [...]

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• RIMNI SPECIAL ISSUE - Computational Mathematics in Quantum-Inspired and Neuromorphic Computing

  Scope

  Deadline Date: 30 January 2026

   

  The ability to learn brain connectivity is generally lacking in standard deep learning models used for vision-brain understanding. Quantum computing, however, introduces a new paradigm for model development. This special issue proposes a novel Quantum-Brain approach—a quantum-inspired neural network influenced by both brain connectivity and quantum entanglement. Quantum technologies, including those used in electrical, optical, and medical devices, are advancing rapidly, particularly in communication and computation. Yet, access remains limited to high-tech organizations. To bridge this gap, quantum-inspired metaheuristics combine quantum principles with metaheuristic algorithms, enhancing global search through probabilistic quantum bit representations. These algorithms are valuable in solving complex optimization problems in various industries. Machine learning, especially neural networks, offers highly efficient and accurate function approximation in high-dimensional spaces, opening new possibilities in AI and scientific computing. Quantum computing (QC), drawing from physics, mathematics, and computer science, is attracting significant attention across academia and industry. Alongside exascale systems, neuromorphic hardware (NMH) and QC are transforming high-performance computing, especially in areas like biomolecular simulations. This special issue aims to explore the development, classification, and practical applications of quantum-inspired metaheuristic algorithms, highlighting their role in solving real-world scientific and engineering problems using next-generation computational technologies.

  Scope

  Deadline Date: 30 January 2026

   

  The ability to learn brain connectivity is generally lacking in standard deep learning models used for vision-brain [...]

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• RIMNI SPECIAL ISSUE - Computational Modeling and Simulation of Optoelectronic Systems for Advanced Information Processing

  Scope

  Deadline Date: 30 Septmeber 2026

   

  This special issue focuses on the computational modeling and simulation of optoelectronic systems for advanced information processing, with particular emphasis on synaptic devices. Fueled by recent advances in optical technologies, computing methods, and simulation techniques, optoelectronic synaptic devices are emerging as a powerful solution to meet the growing demands of high-speed, energy-efficient, and scalable computing. These devices offer benefits such as high bandwidth, low resistance-capacitance delay, reduced energy consumption, and the capability for parallel neural control, making them highly suitable for future information systems and environmental sensing.

  The integration of optoelectronic materials and technologies—such as semiconductor-nanotube composites and bacteriorhodopsin analogues—demonstrates promise in applications ranging from neuromorphic computing to quantum information processing. With growing concerns around power consumption and interconnection bottlenecks in data-intensive tasks, optics and optoelectronics offer a scalable and environmentally sustainable pathway forward.

  This issue invites submissions addressing innovative designs, simulation frameworks, signal processing techniques, and proof-of-concept studies that leverage machine learning, soft computing, and multi-physics modeling. Contributions that explore both fundamental and application-oriented perspectives—particularly those aimed at scalable, sustainable technologies—are especially welcome. The overarching goal is to push forward the development of next-generation optoelectronic synaptic systems for intelligent, energy-aware computing and sensing applications.

  Scope

  Deadline Date: 30 Septmeber 2026

   

  This special issue focuses on the computational modeling and simulation of optoelectronic systems for advanced [...]

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• RIMNI SPECIAL ISSUE - Computational Modelling and Numerical Techniques with Applications in Dynamical Systems

  Scope

  Deadline Date: 31 December 2025

   

  Introduction:

  The accurate modeling and prediction of complex behaviors in dynamical systems remain critical challenges across various science and engineering disciplines. Recent advancements in computational and numerical methods have significantly enhanced the ability to simulate, analyse, and control these systems with greater precision and efficiency. This Special Issue aims to explore cutting-edge computational techniques that address key challenges in system behaviour, advancing both theoretical understanding and practical applications.

  Aims and Scope:

  The objective of this Special Issue is to provide a platform for original research on computational and numerical techniques with various applications related to modeling, analysis, optimization, etc., in dynamical systems to understand the behavior and design of these challenging systems. Contributions that advance the understanding of dynamical systems, while also addressing critical aspects such as performance optimization, system reliability, and long-term sustainability, will be of particular interest. Contributors are encouraged to emphasize both theoretical innovation and practical relevance.

  Specific areas of interest include, but are not limited to:

  Mechanical vibration, stability, and structural health monitoring

  Dynamic modelling of composite, adaptive, and smart materials

  Simulation of robotic manipulators and flexible mechanical systems

  Hybrid numerical and AI-based methods for system analysis

  Optimization of dynamical systems for performance and reliability

  Stability, bifurcation, and transient analysis in complex dynamical systems

  Fuzzy logic, uncertainty quantification, and stochastic analysis

  Applications in biomechanics, environmental dynamics, and epidemiological modeling

  Scope

  Deadline Date: 31 December 2025

   

  Introduction:

  The accurate modeling and prediction of complex behaviors in dynamical [...]

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• RIMNI SPECIAL ISSUE - Computer Modeling in Civil and Geotechnical Engineering

  Scope

  Deadline Date: 28 February 2026

   

  The rapid advancement of computational technologies over the past few decades has significantly transformed the landscape of civil and geotechnical engineering. Traditional analytical and empirical approaches are increasingly being complemented, or even replaced, by sophisticated computer modeling techniques that allow for more accurate, efficient, and cost-effective solutions to complex engineering problems. From predicting soil-structure interactions to optimizing large-scale infrastructure systems, computer modeling plays a pivotal role in enhancing design reliability, improving construction performance, and supporting sustainability in the built environment.
  Given the growing demand for resilient infrastructure and the complexity of modern geotechnical systems, there is an urgent need for robust modeling tools that can capture the highly nonlinear, heterogeneous, and time-dependent behavior of soils and structures. Advances in numerical methods, such as finite element modeling (FEM), discrete element modeling (DEM), and mesh-free methods, as well as emerging applications of machine learning, AI, and data-driven modeling, have opened new frontiers in this domain.
  This Special Issue aims to showcase recent developments, applications, and future directions in computer modeling within civil and geotechnical engineering. It invites contributions that demonstrate innovative modeling techniques, integration of computational tools with experimental data, and cross-disciplinary approaches that enhance our understanding of complex engineering systems.
  The scope of the Special Issue includes, but is not limited to:
  Numerical simulation of soil-structure interaction
  Constitutive modeling
  Applications of AI and machine learning in geotechnical and civil engineering
  Multiscale and multiphysics modeling approaches
  Simulation of construction processes and infrastructure performance
  Coupled hydro-mechanical and thermo-mechanical modeling
  Risk, reliability, and optimization studies using computational tools
  Integration of digital twins and BIM with civil and geotechnical modeling
  We invite original research papers, state-of-the-art reviews, and case studies that contribute to the theoretical, computational, and practical advancements in civil engineering and geotechnical engineering.

  Scope

  Deadline Date: 28 February 2026

  [...]

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• RIMNI SPECIAL ISSUE - Emerging Technologies in Computational and Engineering Sciences: Innovations, Applications, and Future Trends

  Scope

  Deadline Date: 31 December 2025

   

  This special issue aims to highlight recent advances and innovative applications in computational and engineering sciences, with a focus on the integration of emerging technologies that are reshaping research, development, and industrial practices. From cutting-edge artificial intelligence and machine learning algorithms to digital twins, quantum computing, and intelligent control systems, the convergence of computational methods with traditional engineering domains is enabling unprecedented capabilities across various sectors.
  The issue seeks original research articles, comprehensive reviews, and case studies that address theoretical foundations, methodological innovations, real-world implementations, and interdisciplinary applications. Contributions from fields such as mechanical, electrical, civil, chemical, and computer engineering are welcome, especially those that demonstrate the impact of computational intelligence, simulation, data analytics, and automation in solving complex engineering problems.

  Scope

  Deadline Date: 31 December 2025

  [...]

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• RIMNI SPECIAL ISSUE - Exploration of Soliton Dynamics in the Context of Recent Developments

  Scope

  Submission Deadline: 28 February 2026

   

  Nowadays, soliton dynamics is one of the most intriguing branches of mathematical physics having applications in diverse branches of natural and physical sciences such as optics, biological population models, signals processing, electromagnetics, and so on. The current special issue, entitled "Exploration of Soliton Dynamics in the Context of Recent Developments” aims to explore the forefront of developments in these intertwined domains. Solitons, that exhibit localized wave phenomena with the property of maintaining their energy as well as their shape during propagation, and due to the advent of fast computing tools, it is worth to investigate these areas to develop connections between geometric properties and algebraic methods exploration of this area give a fertile ground for investigating deep connections between algebraic methods.

  This special issue is focused to connect researchers' contributions, for which problems related to innovations in theory of solitons are crucial in their research subjects. In this Special Issue, we kindly invite researchers to submit their original work to shed light on how the solitonic structures are helpful to model complex engineering problems. The collected papers will supply a brief but meaningful description of the principal problems in soliton dynamics and explore a wide diversity of its utilizations in many different topics. Potential topics include but are not limited to the following:

  • Lie Groups analysis of differential equations
  • Applications of Lie Algebraic methods
  • Conservation laws and symmetry
  • Inverse Scattering Method
  • Soliton and Nonlinear Wave Solutions
  • Integrability of Solitons 
  • Bifurcation and chaotic Phenomena

  Scope

  Submission Deadline: 28 February 2026

   

  Nowadays, soliton dynamics is one of the most intriguing branches of mathematical physics having applications in diverse branches of natural and physical [...]

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• RIMNI SPECIAL ISSUE - Future of AI in Multi-Sensor Imaging and Fusion

  Scope

  Deadline Date: 31 July 2026

   

  1) The issue introduction includes the background and the importance of this research area.

  The future of AI is the approach to integrate numerous complementary sensors to overcome the constraints of individual sensors operating separately. Finding a balance between edge computing capabilities and sensor fusion is a critical area of development. Artificial intelligence has the potential to increase workplace productivity, which will benefit people by allowing them to accomplish more work. AI will eventually replace laborious or hazardous jobs, freeing up the human workforce to concentrate on jobs for which they are better suited, including those requiring empathy and creativity. In the realm of artificial intelligence, sensor fusion pertains to the procedure of combining information from various sensors to generate well-informed judgments or deductions. This is a compact, handheld device that is compatible with tablets and smartphones.

  Using AI algorithms, AI ultrasonography assists healthcare professionals in low-resource settings in monitoring pregnancies and identifying high-risk pregnancies. These gadgets don't require additional radiology or obstetric training for nurses and midwives to utilize. Research on crops, illnesses, pests, weeds, soil quality, and climate will all make more use of sensors. Costs for farmers will decrease, and working conditions in the fields and stalls will get better. Autonomous vehicles with actual eyesight will be outfitted with new lidar devices. Handling the heterogeneity and complexity of sensor data, which can have varying formats, resolutions, sample rates, coordinate systems, and error models, is one of the major issues. AI will surpass human power in ways that are unimaginable, changing the face of the planet. In some aspects, it will still fall behind human skills. Its capacity to examine enormous volumes of data, spot trends, and come to well-informed conclusions has created new avenues for growth and increased efficiency. The predictive power of AI will grow dramatically. AI systems will be able to provide extremely accurate forecasts in a variety of industries, including banking, weather, and even medical diagnosis, thanks to their massive data access and sophisticated algorithms.

  AI is used to process data from sensors, including temperature, CO2, and motion sensors, humidity meters, and humidity sensors, in order to regulate smart devices and enhance home comfort. AI won't completely take the position of radiologists. It will, nevertheless, alter the way they operate. Radiologists will have to learn ways to collaborate with AI and adjust to these changes. The enhancement in image quality is among the most noteworthy features. By learning to recognize minute patterns and irregularities that are imperceptible to the human eye, artificial intelligence systems are able to evaluate enormous volumes of imaging data. Wearable sensors and artificial intelligence are two more biosensing technological advancements that aim to customize precision medicine for better medical care. These domains provide enhanced patient data collection and analysis through the amalgamation of biosensors with traditional algorithms and pattern recognition. Artificial Intelligence is anticipated to enhance sectors such as healthcare, manufacturing, and customer service, resulting in better experiences for employees and clients. It does, however, confront difficulties including more regulations, issues about data protection, and concerns about employment losses. Contributions are invited from diverse disciplines and perspectives, focusing on the transformative potential and future advancements of AI in multi-sensor imaging and data fusion.

  2) The aim and scope of the Special Issue shall be highlighted.

  The aim and scope of the special issue focus on exploring the innovative integration of artificial intelligence with multi-sensor imaging and data fusion technologies across various sectors such as healthcare, agriculture, autonomous systems, and smart environments. It seeks to present cutting-edge advancements in AI-driven sensor fusion techniques, addressing challenges like heterogeneous sensor data, edge computing balance, real-time analysis, and privacy concerns. Contributions that demonstrate novel AI applications in precision medicine, smart agriculture, autonomous vehicle perception, wearable biosensing, predictive analytics, and secure sensor networks are especially encouraged. The special issue aims to highlight interdisciplinary research that advances efficient, intelligent multi-sensor data processing to improve decision-making, productivity, and quality of life.

  3) Suggested themes shall be listed.       

  Models of Deep Learning for Fusion of Multisensor Data.       

  Multi-sensor systems using AI-driven image registration.       

  AI Methods for Multi-Sensor Fusion in Autonomous Systems include Real-Time Applications.       

  Multispectral and Hyperspectral Image Fusion Enhanced by AI.       

  AI and Medical Imaging for Integration of Multimodal Data.       

  AI and Edge Processing for Multi-Sensor Fusion.       

  AI for Remote Sensing Multi-Sensor Fusion.       

  Sturdy AI Algorithms for Fusion in Unfavorable Conditions.       

  Intelligent Surveillance Systems using AI Enhanced Sensor Fusion.       

  Fusion of Multiple Sensor Data for AI-Powered Predictive Maintenance.       

  Augmented Human Machine Interaction with AI and Sensor Fusion.       

  Fusion of Sensors in Smart Cities Using AI.

  Scope

  Deadline Date: 31 July 2026

   

  [...]

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• RIMNI SPECIAL ISSUE - Generative AI and Computer Vision Advances, Challenges, and Applications

  Scope

  Deadline Date: 30 June 2026

   

  This special issue explores the integration of Generative AI and Computer Vision with numerical methods for engineering calculation and design. The issue highlights cutting-edge advancements in computational models, including GANs, VAEs, and diffusion models, and their role in solving complex engineering problems through synthetic data generation, image-based simulations, and automated design optimization.

  Key focus areas include:

  • Computational and Numerical Models: Application of generative AI for enhancing finite element analysis, computational fluid dynamics, and other numerical simulations.
  • Computer-Aided Design (CAD) Innovations: AI-driven generative design, 3D reconstruction, and automated prototyping.
  • Machine Learning & AI in Engineering: Vision-based systems for structural health monitoring, autonomous robotics, and industrial inspection.
  • Soft Computing for Engineering Challenges: Addressing uncertainty, optimization, and interpretability in AI-aided engineering solutions.
  • High-Performance Computing: Efficient algorithms to reduce computational costs in large-scale vision and generative models.

  Scope

  Deadline Date: 30 June 2026

   

  This special issue explores the integration of Generative AI and Computer Vision with numerical methods for engineering [...]

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• RIMNI SPECIAL ISSUE - High-Fidelity Finite Element Modeling and Real-Time Condition Monitoring for Industrial Assets

  Scope

  Deadline Date: 15 February 2026

   

  The increasing complexity of modern industrial systems demands advanced tools for ensuring structural integrity, safety, and operational efficiency. This special issue focuses on the integration of high-fidelity Finite Element Modeling (FEM) with real-time condition monitoring and intelligent systems for predictive maintenance and structural health management. By merging numerical simulation precision with sensor-based monitoring and emerging digital twin technologies, this interdisciplinary approach facilitates dynamic, condition-aware decision-making in industries such as aerospace, energy, transportation, and civil infrastructure.

  We invite research contributions that explore the development and application of intelligent technologies such as AI, machine learning, IoT, robotics, and real-time data analytics for enhancing FEM frameworks. Emphasis is placed on adaptive simulations, anomaly detection, reduced-order modeling, and physics-informed neural networks (PINNs) for scalable, real-time asset monitoring. Contributions highlighting applications in digital twins, fatigue life prediction, and smart maintenance of critical systems like pipelines, turbines, and offshore structures are especially encouraged. This issue aims to provide a comprehensive overview of cutting-edge solutions that bridge the gap between physical systems and their virtual counterparts to optimize performance, reduce operational costs, and promote sustainable asset management.

  Suggested Topics of Interest include (but are not limited to):

  • Robotics-Aided Inspection and Model-Referenced Monitoring of Infrastructure Assets
  • Thermal-Structural Digital Twins for Early Failure Detection in Energy Equipment
  • AI-Powered Condition Monitoring Framework for Smart Industrial Pipelines
  • Machine Learning Enhanced Structural Models for Critical Asset Management
  • Neural Network-Assisted Real-Time FEM Updates for Dynamic Load Environments
  • Mechatronic Integration for Fault Prediction Using Advanced Numerical Simulations
  • Big Data-Driven Optimization of FEM Workflows for Industrial Equipment Monitoring
  • Integrated Sensing and Real-Time FEM for Predicting Fatigue in High-Stress Components
  • Condition-Aware Numerical Modeling Frameworks for Sustainable Industrial Operations
  • Surrogate Modeling for High-Speed Predictive Maintenance in Industrial Digital Twins
  • Cloud-Based Condition Assessment Using Adaptive FEM and Intelligent Sensing
  • Vibration Analytics and Model-Driven Insights for Rotating Equipment Maintenance
  • Robotics-Aided Inspection and Model-Referenced Monitoring of Infrastructure Assets 

  Scope

  Deadline Date: 15 February 2026

   

  The increasing complexity of modern industrial systems demands advanced tools for ensuring structural integrity, safety, [...]

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• RIMNI SPECIAL ISSUE - Hybrid Intelligence Models and Numerical Techniques in Computational Engineering: Trends and Applications

  Scope

  Deadline Date: 31 March 2026

   

  The increasing complexity of engineering systems in the modern digital age requires advanced approaches that combine the strengths of human reasoning and artificial intelligence (AI). Hybrid Intelligence, a multidisciplinary field that merges human cognitive abilities with AI-based computational systems, is emerging as a key factor in enhancing performance, adaptability, and decision-making in engineering design and analysis. This convergence is changing how numerical methods are developed and used in computational engineering. Numerical techniques are still essential for solving many engineering problems, such as structural mechanics, thermal analysis, fluid dynamics, materials simulation, and control system design. However, traditional methods often face challenges in scalability, real-time responsiveness, and managing uncertain or incomplete data. By integrating hybrid intelligence into these frameworks, researchers and engineers can overcome these challenges, resulting in models that are more robust, adaptable, and context-aware.

  In hybrid intelligent systems, AI components like neural networks, fuzzy inference systems, genetic algorithms, and reinforcement learning are integrated into the computational pipeline. These systems not only automate routine simulations but also assist human experts by providing predictive analytics, optimizing computational processes, and adapting to real-time constraints. This human-in-the-loop collaboration creates systems that evolve with expert feedback, improving learning efficiency and interpretability two key challenges in AI-based engineering applications. Additionally, hybrid intelligence supports data-driven modeling approaches that complement traditional numerical simulations. Engineering fields increasingly depend on large amounts of data generated by sensors, digital twins, and simulation platforms. Combining AI models that learn from this data with domain-specific numerical solvers results in systems capable of high-fidelity predictions, uncertainty quantification, and dynamic model adjustments, even when faced with changing boundary conditions or nonlinear behavior.

  This special issue aims to gather original research contributions, review articles, and case studies that explore recent advances in hybrid intelligence methods combined with numerical techniques in engineering. The focus is on showcasing new computational methods, theoretical innovations, software tools, and practical applications where hybrid models improve accuracy, speed, or efficiency in engineering simulations and design processes. Submissions are welcomed that demonstrate applications across various engineering fields, including but not limited to civil, mechanical, aerospace, electrical, materials, and computational engineering. Contributions providing experimental validation, algorithmic benchmarking, or interdisciplinary insights between numerical mathematics and AI are especially encouraged.

  Suggested topics include, but are not limited to, the following:

  Hybrid intelligence-based numerical models for engineering applications

  Human-in-the-loop computational frameworks for design and optimization

  Soft computing and machine learning techniques in finite element analysis

  AI-enhanced numerical simulations in mechanical, civil, or structural engineering

  Intelligent decision-support systems in complex engineering environments

  Hybrid intelligent systems for multi-physics and multi-scale modeling

  Evolutionary algorithms and hybrid intelligence in structural optimization

  Data-driven modeling and hybrid prediction systems in engineering design

  Integration of symbolic and connectionist approaches in numerical computation

  Uncertainty quantification and hybrid reasoning in engineering simulations

  Adaptive mesh refinement and hybrid learning-based computational models

  Hybrid AI methods for solving nonlinear partial differential equations

  Collaborative computation systems in computer-aided design (CAD)

  Intelligent automation of engineering workflows using hybrid intelligence

  Scope

  Deadline Date: 31 [...]

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• RIMNI SPECIAL ISSUE - ICRAPS-2026: Computational Modeling and Experimental Insights in Physical Sciences

  Scope

  Deadline Date: 30 June 2026

   

  Background: The physical sciences have witnessed rapid progress through the integration of computational modeling and experimental investigation. With advances in computing power, data analytics, and experimental instrumentation, researchers can now simulate, predict, and validate complex physical phenomena with unprecedented precision. This synergy between computational and experimental approaches not only accelerates scientific discovery but also enables the design and optimization of materials, systems, and technologies across multiple disciplines. 

  Objectives: This special issue seeks to:

  • Highlight the latest advancements in computational and experimental methods across the physical sciences.

  • Bridge the gap between theory, modeling, and experimental validation.

  • Encourage interdisciplinary collaboration between computational scientists and experimental researchers.

  • Promote innovative applications of simulation and laboratory techniques in solving real-world scientific and engineering problems.

  • Provide a platform for emerging trends such as machine learning, data-driven modeling, and multiscale simulations. 

  Topics: Potential topics include (but are not limited to):

  • Computational and experimental thermodynamics

  • Molecular dynamics and simulations

  • Quantum and condensed matter physics

  • Nanomaterials and nanoscale characterization

  • Optical, electronic, and magnetic materials

  • Fluid dynamics and transport phenomena

  • Computational chemistry and spectroscopy

  • Data-driven discovery and inverse design

  • Advanced laboratory techniques and instrumentation

  • Integration of simulation, data science, and experimental physics 

   

   

  Scope

  Deadline Date: 30 June 2026

   

  [...]

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• RIMNI SPECIAL ISSUE - Innovative Analytical, Semi-Analytical, and Computational Approaches for Solving Ordinary and Partial Differential Equations

  Scope

  Deadline Date: 31 March 2026

   

  Differential equations are essential for modeling and analyzing phenomena across the various engineering domains. While traditional analytical methods exist, recent decades have seen significant advancements in semi-analytical and computational methods, making it possible to solve complex, nonlinear, and multi-physics systems that were previously unsolvable. The emergence of hybrid approaches and high-performance computing is transforming how we approach these equations.

  This Special Issue aims to be a hub for cutting-edge research on these advanced methods for solving ordinary and partial differential equations. We seek contributions that highlight both foundational developments and real-world, interdisciplinary applications.

  By bringing together diverse contributions, the issue will provide a comprehensive overview of modern solution strategies, encouraging the integration of classical theory with modern computational tools. It will also foster collaborations among mathematicians, computational scientists, and applied researchers, offering valuable benchmarks and insights for both theoretical and practical applications across various fields. This collection is unique in its unified focus, exploring the synergy between different solution techniques to advance the field.

   

  Approximate and Semi-Analytical Approaches: These methods provide solutions that are not exact but are highly accurate. They're especially useful for problems where finding an exact solution is impossible.

  •     Applications: Quantum mechanics (solving the Schrödinger equation for complex systems), fluid dynamics (modeling boundary layers), and engineering (analyzing vibrations in structures).

   

  Numerical and Computational Methods: These involve using algorithms and computers to find approximate solutions to differential equations. They are critical for high-dimensional or non-linear problems.

  •     Applications: There are various scientific field that require the numerical methods for the analysis of the under phenomenon. For example: climate modeling (predicting weather patterns), pattern formation, and computational biology (simulating protein folding and drug interactions).

  Scope

  Deadline Date: 31 March 2026

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• RIMNI SPECIAL ISSUE - Intelligent Computational Methods and Innovations in Engineering

  Scope

  Deadline Date: 31 December 2025

   

  Intelligent Computational Methods and Innovations for Industry 4.0

  Engineering design can be optimized for greater precision, accuracy, and efficiency by integrating intelligent computational methods with high-performance computing, multi-physics simulations, digital twin technology, soft computing techniques, statistical analysis, and real-time decision-making. This Special Issue aims to present cutting-edge research encompassing the theory and applications of intelligent methods and innovations in engineering design, covering but not limited to the following sub-topics:

  1. Advanced finite element methods

  2. Soft computing for engineering optimization

  3. Advance machine learning for Industry 4.0

  4. AI-driven design, automation, and optimization

  5. Computational fluid dynamics advancements

  6. AI-based structural health monitoring and control

  7. Intelligent mesh generation techniques

  8. Optimization algorithms for engineering design

  9. Hybrid computational methods for design

  10. High-performance computing applications

  11. Uncertainty quantification in simulations

  12. Multi-physics numerical modeling

  13. Novel neural networks for Industry 4.0

  14. Smart material modeling techniques

  15. Digital twin technology in engineering

  Scope

  Deadline Date: 31 December 2025

   

  Intelligent Computational Methods and Innovations for Industry 4.0

  Engineering [...]

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• RIMNI SPECIAL ISSUE - Intelligent Modeling, Simulation and Decision Making in Complex Engineering Systems

  Scope

  Deadline Date: 31 March 2026

   

  The rapid advancement of engineering systems has led to increasing complexity, uncertainty, and the need for intelligent solutions in modeling, simulation, and decision-making. This special issue invites high-quality research that explores the cutting edge of intelligent modeling, simulation, and decision-making methodologies applied to a broad spectrum of engineering challenges. We are particularly interested in contributions that harness the power of artificial intelligence, machine learning, data science, uncertainty modeling, and advanced computational techniques to address intricate problems within engineering systems.
  This issue aims to provide a platform for researchers and practitioners to present novel theoretical frameworks, innovative algorithms, and practical applications that enhance our ability to understand, predict, and control complex engineering phenomena. We encourage papers that demonstrate significant advancements in various engineering domains, such as civil, mechanical, aerospace, electrical, environmental, and manufacturing engineering, as well as interdisciplinary applications. Topics of interest include, but are not limited to:
  Intelligent numerical methods for modeling complex engineering systems;
  Uncertainty modeling and analysis in complex engineering systems;
  Applications of machine learning and deep learning in engineering modeling and simulation;
  Data-driven decision support systems for engineering applications;
  Intelligent information fusion methods for complex engineering problems;
  Hybrid intelligent systems for modeling and optimization;
  Integration of intelligent modeling and simulation in decision support systems;
  Human-in-the-loop and explainable AI for engineering decision-making;
  Real-time monitoring, prediction, and health management of engineering systems.
  By fostering the exchange of ideas across diverse engineering disciplines, this special issue seeks to drive the next generation of intelligent solutions for complex engineering systems, ultimately contributing to more robust, efficient, and adaptive designs and operations.

  Scope

  Deadline Date: 31 March 2026

  [...]

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• RIMNI SPECIAL ISSUE - Machine Learning, Surrogate Models, and Optimization in Engineering Design

  Scope

  Deadline Date: 30 June 2026

   

  The increasingly complex engineering problems demand new, robust, and synergic methodological approaches, able to provide new and innovative solutions. This special issue is dedicated to exploring the fields of machine learning, surrogate modeling, and optimization in computational mechanics, highlighting the latest advancements in these areas and their intersections.

  The wide scope of this special issue includes machine learning algorithms able to create new high-quality models as well as the establishment of advanced optimization frameworks integrating metamodels.

  Specific areas of interest include but are not limited to: Machine Learning, Artificial Intelligence, Surrogate Models, Optimization, Engineering Design, Computational Mechanics, Computational Design, High-Fidelity Models, Uncertainty Quantification, Computational Fluid Dynamics, Finite Element Analysis, Data-Driven Design, AI-Accelerated Engineering, among other topics.

  Keywords: Machine Learning, Surrogate Models, Optimization, Computational Mechanics, Computational Modelling and Design

  Scope

  Deadline Date: 30 June 2026

   

  [...]

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• RIMNI SPECIAL ISSUE - Mathematical Modeling and Algorithmic Solutions for Energy-Efficient IoT Networks

  Scope

  Deadline Date: 31 March 2026

   

  The Internet of Things (IoT) has become a transformative force across numerous engineering and technological fields, enabling extensive interconnection of devices through sensors, actuators, and embedded communication systems. However, the exponential growth of IoT devices poses serious challenges in terms of energy efficiency and resource optimization. Power constraints, dynamic network demands, and non-linear resource allocation problems make the efficient operation of IoT networks increasingly complex. Addressing these challenges requires advanced mathematical modeling and algorithmic approaches capable of optimizing energy usage while maintaining high Quality of Service (QoS). Innovative solutions, including heuristic and metaheuristic algorithms, fuzzy logic, and dynamic optimization frameworks, are essential to achieve sustainable, adaptive, and resilient IoT ecosystems.

  This special issue aims to gather high-quality research contributions that focus on mathematical and computational methods to enhance energy efficiency and resource utilization in IoT networks. We seek novel theoretical models, practical algorithmic frameworks, and integrated systems that address multi-objective optimization, dynamic resource allocation, and renewable energy integration. The scope includes but is not limited to intelligent energy management for smart buildings and cities, adaptive IoT infrastructures, and real-time energy-aware monitoring systems.

  Suggested themes:

  • Nonlinear and multi-objective optimization for IoT energy efficiency
  • Heuristic and metaheuristic algorithms (e.g., PSO, Chaotic PSO, AIS)
  • Fuzzy logic-based control and uncertainty management in IoT
  • Dynamic resource allocation frameworks for IoT networks
  • Energy harvesting and renewable energy integration in IoT systems
  • Scalable architectures for energy-aware IoT deployments
  • Real-time monitoring and control of energy flows in intelligent infrastructures
  • Secure and energy-efficient cross-layer frameworks for IoT and 6G clouds
  • AI-driven adaptive systems for resource and energy optimization
  • Mathematical models for sustainable autonomous systems and urban platforms

  Scope

  Deadline Date: 31 March 2026

   

  The Internet of Things (IoT) has become a transformative force across numerous engineering and technological fields, [...]

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• RIMNI SPECIAL ISSUE - Mathematical Modeling and Theory in the Evolution of Artificial Intelligence

  Scope

  Deadline Date: 28 February 2026

   

  The technique of making a computer, a robot controlled by a computer, or software think intellectually like the human mind is recognized as artificial intelligence. AI is achieved by examining the cognitive process and the patterns found in the human brain. Intelligent software and systems are developed as a result of these studies. Last but not least, AI-based mathematical modeling is a relatively new technology that solves challenging physics or biology issues more precisely than ever before by fusing deep learning algorithms with more conventional techniques like linear regression or neural networks. Calculus helps AI systems learn and get better over time by reducing errors. Statistics and Probability: Data is the foundation of AI, yet it is rarely flawless. AI can deal with uncertainty thanks to probability and statistics, which enable it to make defensible conclusions even in the face of inadequate data. A real-world problem is transformed into a mathematically well-posed problem by mathematical modeling, which is an iterative, cyclical process that is then analyzed mathematically and its answer interpreted in terms of real-world restrictions.

  Quantities that rise or fall in accordance with an exponential curve are described by models of exponential growth and exponential decay. Analytical thinking, a critical ability in AI, can be improved by mathematics. Despite popular belief, artificial intelligence is not magic. The magic underlying these inventions comes from mathematics. Finding patterns and correlations in data is essential for activities like anomaly detection, picture recognition, and natural language processing. Applications of mathematics include measurement, statistics, and personal finance, bringing mathematics into the real world. Students gain the knowledge, comprehension, and abilities necessary to apply mathematical concepts and techniques in practical settings. It helps us anticipate future events, make well-informed judgments, and simplify and comprehend complicated systems. The many uses of mathematical modeling, ranging from managing pandemics to space exploration, highlight how essential it is to expanding knowledge and solving global issues.

  Numerical constants and variables that represent various system components are frequently included in mathematical models. The kinematic equations from physics, for instance, can be used to explain how a baseball moves through the air after being pitched. The process of creating an abstract model in mathematical terms to explain the intricate behavior of an actual system is recognized as mathematical modeling. Ordinary differential equations and partial differential equations are frequently used to express mathematical models, which are quantitative models. A good mathematical model should be straightforward, accurate, and useful in practical settings. One of the most important qualities of a good mathematical model is accuracy. A high level of precision in result prediction should be possible using the model. Learn how evolutionary computation models the real world and identifies the most effective, efficient, lowest cost, and/or highest revenue producing outcomes, so helping to solve challenges that human decision making may overlook. Artificial intelligence models are its virtual brains. An algorithm becomes an AI model after it has been trained using data. The accuracy of the model increases with the amount of data it contains. Machine learning, supervised learning, unsupervised learning, and deep learning are a few of the several kinds of AI models. We invite submissions from across disciplines and perspectives, without being confined to specific domains: Mathematical Modeling and Theory in the Evolution of Artificial Intelligence.

  List of topics relevant to this special issue include but are not limited to:

  • Neural Networks' Mathematical Underpinnings: Progress in Comprehending Deep Learning Architectures.
  • AI Optimization Algorithms: Conceptual Understanding and Real-World Uses.
  • Bayesian techniques and probabilistic models in the development of artificial intelligence.
  • Generalized Learning Algorithms in AI Systems: Mathematical Theories.
  • Artificial Intelligence: Using Game Theory to Model Strategic Decision-Making.
  • Tensor Calculus and Matrix Factorization: The Function of Linear Algebra in Contemporary AI.
  • AI behavior modeling using differential equations and dynamical systems.
  • Applications of Graph Theory to Artificial Intelligence: Progress in Knowledge Representation.
  • Mathematical Models for AI Reliability and Robustness in the Face of Uncertainty.
  • Investigating Spaces of Solutions for Optimization Issues through Functional Analysis in AI.
  • Complexity Theory in Artificial Intelligence: Assessing Scalability and Algorithmic Efficiency.
  • AI Using Symbolic Mathematics to Model Reasoning and Logic Mechanisms.

  Scope

  Deadline Date: 28 February 2026

   

  The technique of making a computer, a robot controlled by a computer, or software think intellectually like the [...]

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• RIMNI SPECIAL ISSUE - Mathematical Modelling, Numerical Methods, Analysis and Optimization of Renewable Energy System

  Scope

  Deadline Date: 30 September 2025

   

  The global shift towards renewable energy is driven by the urgent need to address climate change, reduce greenhouse gas emissions, and ensure sustainable energy security. Renewable energy systems, including solar, wind, hydroelectric, and biomass, are increasingly recognized as viable alternatives to fossil fuels. However, the complexity of these systems necessitates advanced mathematical modeling and numerical methods to optimize their performance and integration into existing energy infrastructures.

  Mathematical modeling provides essential insights into the behavior of renewable energy systems under varying conditions. It allows researchers to simulate diverse scenarios, assess system dynamics, and predict energy output. When combined with robust numerical methods, these models can effectively address the intricate interactions within renewable energy networks. Additionally, optimization techniques are vital for improving resource allocation and operational strategies. By leveraging advanced algorithms and optimization frameworks, stakeholders can maximize energy production while minimizing costs and environmental impacts.

  This special issue aims to gather innovative research that explores these critical aspects of renewable energy systems. We invite contributions that not only advance theoretical understanding but also offer practical solutions for real-world applications. Potential topics of interest include, but are not limited to:

  • Mathematical Modeling of Renewable Energy Sources
  • Numerical Methods for Energy System Analysis
  • Optimization Strategies in Renewable Energy Systems
  • Integration of Renewable Energy into Power Grids
  • Performance Evaluation and Sensitivity Analysis
  • Emerging Technologies in Renewable Energy
  • Decentralized Energy Systems and Microgrids
  • AI and Machine Learning Applications in Energy Management

  Scope

  Deadline Date: 30 September 2025

   

  The global shift towards renewable energy is driven by the urgent need to address climate change, reduce greenhouse gas emissions, and ensure sustainable energy security. [...]

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• RIMNI SPECIAL ISSUE - Modern Statistical Models and Machine Learning Models and their Applications in Different Sciences

  Scope

  Deadline Date: 30 August 2026

   

  The special issue "Modern Statistical Models and Machine Learning Models and Their Applications in Different Sciences" brings together innovative research that connects statistical theory and machine learning to practical applications in a variety of scientific fields. This issue highlights the evolving landscape where traditional statistical approaches are being enhanced or reimagined through machine learning techniques to tackle complex, high-dimensional, and non-linear problems. The selected papers cover a broad spectrum of disciplines including medicine, environmental science, finance, engineering, and social sciences, showcasing how these models improve prediction accuracy, decision-making, and pattern recognition.

  A key aspect of the issue is the integration of interpretability and robustness into the development of models, ensuring that these advanced tools can be trusted and used effectively in real-world scenarios. Contributions range from theoretical advancements in statistical modeling to practical case studies demonstrating the real-world benefits of machine learning algorithms such as deep learning, ensemble methods, and probabilistic models. By fostering interdisciplinary collaboration, this special issue serves as a platform for researchers and practitioners to share insights, methodologies, and novel applications. It emphasizes the importance of model validation, ethical AI, and data-driven discovery, making it a valuable resource for those interested in the intersection of statistics, machine learning, and applied sciences.

  Scope

  Deadline Date: 30 August 2026

   

  The special issue "Modern Statistical Models and Machine Learning Models and Their Applications in Different [...]

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• RIMNI SPECIAL ISSUE - Neural network algorithm and image processing

  Scope

  Deadline Date: 30 June 2027

   

  Computer vision and pattern recognition stand at the forefront of artificial intelligence, integrating multidisciplinary advances in machine learning, signal processing, and mathematical theory. In recent years, deep learning techniques—particularly convolutional neural networks (CNNs)—have dramatically reshaped the landscape of these fields. CNNs excel at automatically learning discriminative feature representations directly from raw pixel data, enabling unprecedented performance across tasks such as object recognition, semantic segmentation, image synthesis, and scene interpretation. The growing ubiquity of vision-based systems in real-world applications underscores the increasing importance of innovative research in this area.

  This Special Issue aims to compile cutting-edge research advances and practical applications within computer vision and pattern recognition. We seek to highlight novel methodologies, systematic reviews, and impactful case studies that reflect the latest trends and solutions. The issue will provide a platform for researchers and practitioners to share insights that bridge theoretical innovation and real-world deployment, fostering further development in intelligent visual understanding.

  Suggested Topics:

  We invite original contributions including, but not limited to, the following themes:

  Object detection, recognition, and tracking

  Image and video segmentation

  Deep representation learning and feature encoding

  Generative models for image synthesis and enhancement

  Scene understanding and semantic interpretation

  Large-scale visual recognition and retrieval

  Multimedia information processing and analysis

  Interdisciplinary applications of computer vision in healthcare, robotics, surveillance, and remote sensing

  Submissions should present innovative ideas, solid empirical validation, and potential for broad impact within and beyond the research community.

  Scope

  Deadline Date: 30 June 2027

   

  Computer vision and pattern recognition stand at the forefront of artificial intelligence, integrating [...]

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• RIMNI SPECIAL ISSUE - Numerical Approaches for Solving Engineering Optimization Problems Using Metaheuristic Frameworks

  Scope

  Deadline Date: 15 March 2026

   

  Optimization in engineering is a key contribution to enhancing the functionality, efficiency, and dependability of systems in a wide range of domains including structural mechanics, energy systems, fluid dynamics, and control processes. Most of these optimization problems are nonlinear, strongly constrained, or formulated over-complicated solution spaces that render it hard to solve such problems with the use of conventional numerical approaches alone. To overcome such limitations, metaheuristic frameworks have attracted much consideration due to their flexibility and problem-solving ability. Such frameworks do not depend on gradient information or rigid mathematical formulations that enable them to search complex and large spaces with flexibility. The iterative nature encourages exploration and exploitation and results in feasible solutions even if exact methods are not feasible. More researchers are combining metaheuristics with well-known numerical methods to form hybrid schemes with improved solution accuracy and reliability. These developments are pushing the scope of problems that can be handled successfully in engineering. The increasing applicability of these techniques highlights the necessity for ongoing investigation of their combinations with computational modeling and simulation methods.

  Despite their strengths, applying metaheuristic algorithms to engineering problems presents several challenges. The performance of these algorithms depends heavily on the nature of the problem and selecting the most suitable strategy often requires expert knowledge. Parameter tuning, maintaining search diversity, and avoiding premature convergence remain ongoing concerns. Also, when metaheuristics are used in conjunction with computationally intensive high-fidelity numerical simulations such as iterative solvers or detailed models computational expenses may become quite high. In safety-critical or real-time environments predictable non-convergence and instability of measuring solution quality may also constrain their application. Also, inconsistency in benchmarking throughout research prevents objective algorithm performance comparison. These challenges invite more systematic frameworks, adaptive algorithmic designs, and increased validation practice emphasis. Future research will address more efficient hybrid models, increased interpretability, and the exploitation of advances in parallel computing to scale up metaheuristic optimization and make it more reliable within engineering applications.

  We thus suggest a targeted set of research to further the convergence of metaheuristic platforms with numerical techniques in engineering optimization. This special issue will feature empirical, theoretical, and computational results that solve practical engineering problems. Interdisciplinary insights are invited on novel algorithmic formulations, combined approaches, and verification techniques to enhance the scalability and robustness of engineering solutions.

  Potential topics include but are not limited to the following:

  • Hybrid Metaheuristic-Numerical Frameworks for Engineering Optimization.
  • Scalable Metaheuristic Algorithms in Simulation-Driven Engineering Design.
  • Empirical Evaluation of Metaheuristic Methods in Complex Engineering Applications.
  • Adaptive Parameter Control in Metaheuristic Engineering Systems.
  • Parallel and Distributed Metaheuristics for Large-Scale Engineering Optimization.
  • Uncertainty-Aware Metaheuristic Approaches for Engineering Simulations.
  • Integration of Metaheuristics with Finite Element Analysis in Mechanical Design.
  • Interpretable Metaheuristic Models for Engineering Decision Support.
  • Metaheuristic Strategies for Multi-Objective Engineering Design Problems.
  • Next-Generation Metaheuristics for Cyber-Physical and Control Systems.
  • Validation Frameworks for Metaheuristic-Based Engineering Optimization.
  • Metaheuristic-Driven Innovation in Engineering Design Automation.
  • Cross-Disciplinary Applications of Metaheuristics in Computational Engineering. 

  Scope

  Deadline Date: 15 March 2026

   

  Optimization in engineering is a key contribution to enhancing the functionality, efficiency, and dependability of systems [...]

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• RIMNI SPECIAL ISSUE - Numerical Methods and Computational Simulations in Physical Sciences: Advances and Challenges

  Scope

  Deadline Date: 1 September 2025

   

  Background: Numerical and computational simulations have revolutionized the field of physical sciences, enabling researchers to model, analyze, and predict complex phenomena with unprecedented accuracy. This special issue aims to provide a platform for researchers to share their latest advancements in numerical and computational simulations, highlighting innovative methods, techniques, and applications in physical sciences.

  Objectives:

  To showcase recent developments in numerical and computational simulations in physical sciences.

  To provide a forum for researchers to share their experiences, challenges, and successes in simulating complex physical phenomena.

  To highlight innovative applications of numerical and computational simulations in physical sciences.

  Topics:

  Computational fluid dynamics

  Numerical heat transfer

  Simulation of complex systems

  Computational materials science

  Numerical methods for partial differential equations

  High-performance computing

  Multiscale modeling

  Uncertainty quantification

  Data-driven discovery

  Machine learning in physical sciences

  Importance: Numerical and computational simulations have become an indispensable tool in physical sciences, playing a vital role in advancing our understanding of complex phenomena and driving innovation. The importance of this research area can be highlighted from several perspectives:

  Advancing Fundamental Understanding

  Driving Technological Innovation

  Addressing Societal Challenges

  Fostering Interdisciplinary Collaboration

  Enabling Data-Driven Discovery

  Preparing the Next Generation of Researchers

  Research in numerical and computational simulations provides valuable training for students and early-career researchers, equipping them with essential skills in programming, data analysis, and problem-solving.

  By advancing fundamental understanding, driving technological innovation, addressing societal challenges, fostering interdisciplinary collaboration, enabling data-driven discovery, and preparing the next generation of researchers, numerical and computational simulations in physical sciences play a vital role in shaping our understanding of the world and addressing complex challenges.

  Scope

  Deadline Date: 1 September 2025

   

  Background: Numerical and computational simulations have revolutionized the field of physical sciences, [...]

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• RIMNI SPECIAL ISSUE - Numerical Methods for Fractional Equations in Engineering

  Scope

  Deadline Date: 28 February 2026

   

  Fractional equations have gained significant attention in recent years due to their ability to model complex phenomena in various fields of engineering, such as mechanical, electrical, and civil engineering. These can describe non-local and non-integer order behavior of systems. It is not often possible to solve such engineering problem analytically so numerical methods have become essential tools for approximating solutions.

  The aim of numerical methods for fractional equations in engineering is to develop and apply efficient and accurate numerical techniques to solve fractional equations that model complex phenomena in various fields of engineering. The scope of these methods is to provide a framework for simulating, analyzing, and optimizing systems that exhibit non-integer order behavior, including: viscoelastic materials, electrical circuits, control systems, stochastic processes, heat transfer, etc.

   

  The primary objectives of numerical methods for fractional equations in engineering are:

  1. Numerical Solution

  To develop numerical methods that can solve fractional equations.

  2. Efficient Computation

  To design numerical methods that are computationally efficient, scalable, and can handle large-scale problems.

  3. Interdisciplinary Applications

  To apply numerical methods for fractional equations to a wide range of engineering disciplines, including mechanical, electrical, civil, and aerospace engineering.

   

  The scope of numerical methods for fractional equations in engineering includes, but is not limited to:

  1. Modeling and Simulation

  2. Optimization and Control

  3. Data Analysis and Interpretation

   

  The key challenges in achieving the aim and scope of numerical methods for fractional equations in engineering include:

  1. Handling the mathematical complexity of fractional equations, including the lack of analytical solutions and the need for numerical approximations.

  2. Developing numerical methods that are computationally efficient and can handle large-scale problems.

  3. Collaborating with experts from various engineering disciplines to develop and apply numerical methods for fractional equations.

   

  Suggested themes shall be listed.

  • fractional calculus in theoretical physics and mechanics
  • mathematical modeling of media with memory
  • viscoelastic models with fractional order operators

  Scope

  Deadline Date: 28 February 2026

   

  Fractional equations have gained significant attention in recent years due to their ability to model complex phenomena in various fields of engineering, such as mechanical, [...]

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• RIMNI SPECIAL ISSUE - Numerical Methods for Nonlinear Mathematical Models with Engineering Applications

  Scope

  Deadline Date: 30 April 2026

   

  This special issue on Numerical Methods for Nonlinear Mathematical Models with Engineering Applications is dedicated to exploring and advancing the development of computational approaches for the analysis and solution of complex mathematical models arising in engineering sciences. These models are critical for understanding various phenomena such as heat transfer, fluid dynamics, and other related areas where traditional analytical techniques often fall short due to the inherent nonlinearities and complexities involved. In particular, this issue places a strong emphasis on recent progress in the field of numerical methods for fractional differential equations (FDEs), especially those of a time-fractional nature. These equations, which extend classical integer-order differential equations, have become a powerful tool in modeling processes with memory, hereditary effects, and anomalous dynamics. Their applications are widespread and significant, encompassing disciplines such as viscoelastic material behavior, transport in porous media, anomalous diffusion, bioengineering, signal processing, and control systems.

  The complexity of these models is further compounded when they include nonlinear terms, time delays, singular or nonsmooth initial conditions, and coupled systems. These features pose substantial challenges not only in theoretical analysis but also in the development of accurate and efficient numerical solvers. The nonlocal and singular nature of fractional derivatives requires innovative computational strategies to ensure both stability and accuracy over long time simulations.

  This special issue aims to gather high-quality original research articles and review papers that propose novel numerical algorithms, provide rigorous error analysis, or demonstrate significant engineering applications of these methods. We also welcome contributions that incorporate machine learning techniques including physics informed neural networks, operator learning, and data driven surrogate modeling. These approaches are especially useful for accelerating simulations and approximating complex fractional dynamics where analytical solutions are unavailable. Topics of interest include, but are not limited to, finite difference and finite element methods for FDEs, spectral methods, meshless methods, and hybrid approaches tailored for nonlinear fractional systems. Contributions addressing the modeling, simulation, and real-world applications of such methods in engineering contexts are especially welcome.

  Scope

  Deadline Date: 30 April 2026

   

  This special issue on Numerical Methods for Nonlinear [...]

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• RIMNI SPECIAL ISSUE - Numerical Methods for Optimizing Grid Stability in Solar Power Integration

  Scope

  Deadline Date: 15 March 2026

   

  The shift to global low-carbon energy systems has boosted the popularization of solar photovoltaic (PV) technology, shifting the power generation landscape. Solar power presents many environmental and economic advantages but integration into the installed base of power systems is extremely challenging from a technical perspective because the solar energy supply is intermittent and variable. These generation output fluctuations can destabilize voltage and frequency, impact power quality, and undermine grid reliability, especially in systems with high solar penetration. To counter these threats, sophisticated numerical techniques have become critical to model, simulate, and optimize the performance of power systems interconnected with distributed and utility-scale solar PV units. This research focus not only enables the credible integration of solar power into contemporary electric grids but also works toward the building of resilient, smart grid structures. Relying on sound numerical approaches guarantees accurate performance projections, design decisions based on solid information, and cost-efficient control schemes that enhance the sustainability and reliability of next-generation energy systems. The integration requires a good knowledge of dynamic system interaction, changes in loads, inverter control strategies, and coordination of energy storage all of which can be easily learned by computational modeling methods.

  Numerical simulation and modeling methods, including Finite Element Method (FEM), Finite Difference Method (FDM), and numerical time-domain analysis offer a mathematical formulation to mimic the power flow dynamics, transient stability, harmonic distortion, and voltage fluctuations in grid-tied PV systems. Power flow simulations and stability tests using nonlinear differential equations play a crucial role in comprehending the effects of high PV penetration. Software: Newton-Raphson load flow solvers and dynamic stability programs assist in finding critical operating points that lead to voltage collapse or frequency instability. In addition, multi-objective optimization algorithms, including Genetic Algorithms, Particle Swarm Optimization (PSO), and Sequential Quadratic Programming (SQP) are used more and more to optimize inverter control strategies and optimize the sizing and dispatch of energy storage systems. A novel area of focus is the application of reduced-order models (ROMs) and physics-informed neural networks (PINNs), which yield computationally effective surrogates to the complex grid models without sacrificing the fundamental physical laws.  The numerical optimization is also applied to the area of reactive power compensation, fault ride-through, and grid code compliance.

  This special issue welcomes high-quality original research, technical reviews, and papers dealing with numerical modeling, algorithmic formulation, and simulation-based optimization of solar power integration into power grids. The emphasis is placed on the use of finite element methods (FEM), finite difference methods (FDM), optimization algorithms, power flow analysis, dynamic system modeling, and control theory to tackle the multi-scale, multi-domain issues involved in PV-grid coupling. We especially encourage submissions that connect theoretical modeling with real-world applications, such as verified simulation platforms, and hardware-in-the-loop (HIL) tests providing real-world grid integration performance demonstration.

   

  Scope

  Deadline Date: 15 March 2026

   

  The shift to global low-carbon energy systems has boosted the popularization of solar photovoltaic (PV) technology, [...]

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• RIMNI SPECIAL ISSUE - Numerical Modeling of Swarm Intelligence for Real-World Engineering Optimization

  Scope

  Deadline Date: 10 April 2026

   

  Swarm Intelligence (SI) is a nature-inspired computational approach modeled on the collective behavior of organisms like birds, ants, and fish. Characterized by decentralized control, self-organization, and adaptability, SI is effective in solving complex, nonlinear, and high-dimensional optimization problems where traditional methods fall short. Numerical modeling plays a vital role in transforming these bio-inspired ideas into engineering algorithms, allowing for simulation-based performance analysis, stability assessment, and parameter tuning. Higher-order SI algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) have demonstrated success across engineering fields. In civil engineering, they optimize truss and bridge structures; in electrical engineering, they assist in load balancing and network configuration; and in mechanical and aerospace engineering, they enable system tuning and aerodynamic optimization. These models often incorporate mathematical representations of swarm dynamics and are hybridized with methods like Finite Element Analysis (FEA) or Computational Fluid Dynamics (CFD) for greater accuracy. Integration with machine learning further enhances adaptability. This Special Issue aims to showcase innovative numerical models, hybrid frameworks, and practical applications of SI in engineering. We invite contributions that highlight algorithmic advancements, computational efficiency, and interdisciplinary engineering solutions using SI for real-world optimization challenges.

   

  Scope

  Deadline Date: 10 April 2026

   

  Swarm Intelligence (SI) is a nature-inspired computational approach modeled on the collective behavior of organisms like [...]

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• RIMNI SPECIAL ISSUE - Numerical Modeling, Simulation, and AI-Driven Methods for Dynamical Systems in Engineering and Sciences

  Scope

  Deadline Date: 31 May 2026

   

  This Special Issue aligns with the journal's focus on Computational and Numerical Models of Engineering Problems, Development and Application of Numerical Methods, Advances in Software, Computer Design Innovations, Soft Computing, Machine Learning, and Artificial Intelligence. It aims to explore the theoretical foundations, computational advancements, and practical applications of time-delayed and fractional-order systems, which are critical in modeling complex dynamics across engineering and scientific domains. These systems, where current states depend on historical data or exhibit non-integer order dynamics, pose unique challenges and opportunities for innovative numerical methods, software solutions, and intelligent algorithms.

  The issue emphasizes the development of robust computational models, advanced numerical techniques, and machine learning-driven approaches to address the complexities of time-delayed and fractional-order systems. It also seeks to advance soft computing and AI-based methodologies for stability analysis, control design, and system optimization, fostering interdisciplinary applications in engineering, biomedical sciences, and beyond.

  We invite high-quality submissions that align with the journal's scope, including but not limited to the following topics:

  -Computational modeling and numerical analysis of nonlinear and time-delayed dynamical systems

  -Development of numerical methods for stability analysis of fractional-order systems

  -Advances in software tools for simulation and control of delayed and fractional-order systems

  -Soft computing techniques for robust and adaptive control in uncertain delayed systems

  -Numerical techniques for synchronization and control of chaotic and hybrid systems

  -Computational frameworks for fractional-order and distributed-order control systems

  -Stochastic system modeling and simulation with time delays

  -Delay-dependent Lyapunov-based methods for stability analysis and criteria development

  -Applications of computational models in power systems, communication networks, and robotics

  -AI-driven modeling and simulation of biological, biomedical, and ecological delayed systems

  -Numerical methods for solving ordinary and partial differential equations with delays

  -Cyber-physical system design and security analysis for delayed dynamics

  -Data-driven and machine learning-based approaches for fractional-order system optimization

  Scope

  Deadline Date: 31 May 2026

   

  This Special Issue aligns with the journal's focus on Computational and Numerical Models of Engineering Problems, [...]

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• RIMNI SPECIAL ISSUE - Optimization Algorithms in Artificial Intelligence

  Scope

  Deadline Date: 31 March 2026

  1. Scope and Objectives

  Artificial Intelligence (AI) has emerged as a cornerstone of modern computational science, impacting a vast range of domains including healthcare, finance, robotics, cybersecurity, logistics, and more. At the heart of AI systems lies the crucial role of optimization algorithms — enabling model training, decision-making, resource allocation, and system performance improvements.

  This Special Issue aims to bring together cutting-edge research and innovative applications that leverage optimization algorithms to enhance AI capabilities. By highlighting both theoretical advancements and practical implementations, the issue will provide a comprehensive insight into the current trends, challenges, and future directions in the intersection of AI and optimization.

  2. Topics of Interest

  We invite original research articles, review papers, and case studies on topics including, but not limited to:

  Metaheuristic optimization in AI (e.g., Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization)

  Convex and non-convex optimization methods in deep learning

  Multi-objective and dynamic optimization in AI systems

  Reinforcement learning and reward optimization

  Hyperparameter tuning algorithms for machine learning models

  Swarm intelligence and bio-inspired optimization in AI

  Evolutionary computation for model selection and feature engineering

  Optimization in federated and distributed AI systems

  Energy-efficient and real-time AI optimization techniques

  Benchmarking and comparative studies of AI optimization algorithms

  Scope

  Deadline Date: 31 March 2026

  1. Scope and Objectives

  Artificial Intelligence (AI) has [...]

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• RIMNI SPECIAL ISSUE - Optimization Techniques for Composite Structures: Design, Analysis, and Applications

  Scope

  Deadline Date: 31 May 2026

   

  Optimization of composite structures has become a cornerstone of modern engineering design, offering significant potential for enhancing structural performance, minimizing weight, and improving cost-effectiveness across a wide range of industrial applications — from aerospace and automotive engineering to civil and marine structures. By leveraging optimization methodologies, engineers can systematically determine the optimal combination of materials, fiber orientations, stacking sequences, and geometrical configurations to meet specific mechanical, thermal, and durability requirements under diverse loading and environmental conditions.

  Contemporary optimization approaches such as gradient-based methods, genetic algorithms, topology optimization, and multi-objective metaheuristics have expanded the design space exploration beyond traditional trial-and-error methods. These computational techniques enable designers to efficiently handle complex, nonlinear, and multi-constraint problems inherent to composite materials, leading to innovative, lightweight, and structurally efficient solutions. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) is revolutionizing predictive modeling, surrogate-based optimization, and design automation for composite structures.

  The aim of this special issue is to gather cutting-edge research and novel methodologies that advance the optimization, design, and analysis of composite materials and structures. It seeks to provide a comprehensive platform for showcasing recent progress in algorithmic development, computational modeling, and experimental validation that drive innovation in high-performance and sustainable composite systems.

  This special issue welcomes original research articles, review papers, and case studies addressing (but not limited to) the following areas:

  Advanced optimization methods for the design and analysis of composite structures

  AI- and machine learning–based frameworks for simulation and optimization of composite responses

  Topology and shape optimization of composite components for weight and stiffness efficiency

  Genetic algorithms, metaheuristics, and multi-objective optimization in composite material design

  Multi-scale and multi-physics modeling approaches integrated with optimization strategies

  Surrogate modeling and response surface methods for design space exploration

  Reliability-based and robust design optimization of composite systems

  Experimental validation and data-driven optimization frameworks

  By bridging the gap between theoretical developments and practical applications, this special issue aims to advance the understanding and implementation of optimization-driven design in composite engineering. It aspires to serve as a valuable reference for researchers, practitioners, and industry professionals seeking to develop next-generation composite structures that are lighter, stronger, and more sustainable through intelligent optimization methodologies.

  Scope

  Deadline Date: 31 May 2026

   

  Optimization of composite structures has become a cornerstone of modern engineering [...]

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