The design and optimization of modern engineering thermal systems—such as heat exchangers, electronic cooling devices, renewable energy systems, and geothermal applications—require a deep understanding of complex coupled physical phenomena. These include fluid flow, heat transfer, magnetic field interactions, porous media effects, and geometric complexity. While recent research has increasingly focused on data-driven and artificial intelligence approaches, there remains a critical and ongoing need for robust, high-fidelity numerical methods to solve the underlying fundamental physics described by coupled nonlinear partial differential equations.

This Special Issue aims to bring together original research articles and comprehensive review papers on the development and application of advanced numerical methods for thermo-fluid dynamics in complex engineering systems. Topics of interest include, but are not limited to, natural and mixed convection, nanofluid and hybrid nanofluid flows, magnetohydrodynamic (MHD) effects, flows in porous media (using models such as Darcy, Brinkman, and Forchheimer), heat transfer enhancement strategies, entropy generation analysis, and thermodynamic optimization. The issue seeks to bridge the gap between theoretical numerical developments and their practical application in engineering design, offering a platform for researchers to share cutting-edge computational techniques and their applications.

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).

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.

This special issue aims to present recent advances in iterative methods for solving complex systems, including linear and nonlinear equations, complementarity problems, absolute value equations, and fractional differential equations (FDEs). The focus is on bridging theory and practice, addressing challenges posed by large-scale, nonlocal, and memory-dependent problems.

Iterative techniques remain essential where direct solvers fail due to size, nonlinearity, or nonsmooth structure. Contributions are welcome on novel algorithms, convergence and stability analyses, error estimates, and acceleration strategies. Particular emphasis is placed on iterative methods for FDEs—such as predictor-corrector schemes, finite difference iterations, spectral techniques, and Newton-type solvers—which must handle weakly singular kernels and long-term memory.

Applications span scientific computing, engineering (viscoelasticity, anomalous diffusion), economics, control theory, biology, and data-driven modeling. Hybrid approaches, machine learning-integrated iterative solvers, and large-scale numerical implementations are also encouraged.

Topics of interest for the special issue include, but are not limited to, the following:

General iterative methods for solving linear and nonlinear systems;

Iterative solutions forcomplementarity problems;

Iterative algorithms for absolute value equations;

Iterative methods for fractional differential equations (FDEs);

Numerical iterative schemes for fractional boundary and initial value problems;

Convergence and stability analysis of iterative methods for fractional systems;

Hybrid and accelerated iterative techniques for complex systems;

Convergence and stability analysis of iterative processes;

Optimization and efficiency in iterative methods for large-scale systems;

Applications in scientific computing, engineering, economics, biology, and beyond;

Iterative approaches for nonlinear optimization problems;

Algorithmic developments in iterative schemes for complex equations;

Iterative methods for real-world problems in various industries;

Iterative techniques in data-driven and machine learning applications;

Numerical implementations and computational performance of iterative solvers.

The cement and concrete industry is responsible for a significant share of global CO₂ emissions. It makes the development of low-carbon, high-performance cementitious materials a critical challenge for achieving net-zero targets. This Special Issue focuses on innovative strategies for the formulation, optimization, and performance evaluation of low-CO₂ and green cement-based materials. The particular emphasis will be on machine learning and data-driven approaches.

Topics include but not limited to:

1. The design and optimization of sustainable concrete and cementitious composites incorporating industrial by-products

2. Performance and durability assessment of construction materials

3. Application of machine learning, computer vision, and environment simulation in materials design, carbonation prediction, and durability evaluation.

4. Studies on sustainable assessment frameworks, low-carbon innovation pathways, and circular economy-oriented material utilization.

By integrating advanced computational methods with experimental investigations, this Special Issue aims to promote net-zero optimization of cementitious materials while ensuring mechanical performance, durability, and long-term sustainability. Contributions addressing multi-objective optimization, lifecycle assessment, and digital technologies for green cement and concrete are particularly encouraged. This Special Issue seeks to provide a multidisciplinary platform for researchers and engineers to accelerate the transition toward low-carbon and circular construction materials.

Industrial mathematics concerns the analysis and solution of problems that arise in industries, corporations, government agencies, and other organizational settings. Its purpose is to produce optimal, efficient, and reliable solutions to support decision-making and innovation in finance, environmental sustainability, production systems, resource management, and technology development. As modern industry becomes more complex and sophisticated, there is a growing need for professionals who can understand technical challenges, formulate accurate mathematical models, implement solutions through advanced computational and digital techniques, and communicate these solutions effectively to engineers, workers, managers, and decision makers.

Industrial mathematics is an interdisciplinary area of applied mathematics concerned with complicated problems emerging in industrial engineering and related subjects. It utilizes a wide variety of analytical, computational, and numerical methods, including integral and differential equations, interpolation, polynomial computation, least-squares methods, regularization, vector and matrix algebra, control, synthesis, discrete and continuous transforms, image and signal processing, Galerkin methods, finite-element methods, spectral methods, shooting methods, finite-difference methods, and finite-volume methods. Industrial mathematics combines the power of mathematics, statistics, optimization, engineering, computer science, and business-oriented techniques for modeling, analysis, simulation, and decision-making. These interdisciplinary abilities are becoming increasingly essential in academia, research institutes, and industry, especially as modern technology problems require efficient and mathematically precise solutions.

This Special Issue aims to promote research and applications of industrial mathematics, with a focus on industrial and/or real-world applications of mathematical concepts.

Topics of interest include, but are not limited to, the following:

Operations research and its industrial applications

Statistical methods, modeling, and data analysis

Reliability engineering and risk assessment

Optimization models, algorithms, and methods

Industrial applications of computing and artificial intelligence

Successful case studies demonstrating the role of industrial mathematics

Applied mathematical methods in management, science, and engineering

Mathematical modeling and simulation in fluid mechanics

Engineering disturbances, such as excavation unloading, blast-induced stress waves, hydraulic fracturing, and mechanical rock breaking, are common in deep underground engineering, mining, tunneling, and slope stability projects. These disturbances trigger complex mechanical responses in rock and geo-materials, including damage evolution, fracture propagation, dynamic failure, and coupled multi-field effects (e.g., stress, seepage, damage interactions). A thorough understanding of these responses is essential for the safe and efficient design of engineering structures in challenging geological conditions.

This Special Issue aims to collect high-quality original research and review articles that advance the understanding of rock/geo-material behavior under various engineering disturbances through numerical modeling and experimental investigations.

Topics of interest include, but are not limited to:

Numerical simulation of excavation-induced unloading responses in deep rock masses

Dynamic mechanical behavior under blast or impact loading

Hydraulic fracturing mechanisms and coupled hydro-mechanical processes

Constitutive models for rock damage, fracture, andfailure

Experimental investigation (e.g., cyclic loading, unloading, chemical erosion) for characterizing rock/geo-materials damage

Multi-field coupling (thermal–hydraulic–mechanical–chemical) in deep rock engineering

Cutter-rock interaction and numerical simulation of mechanical tunneling

Case studies involving validation of numerical models against laboratory or field data

Contributions that integrate numerical simulations with laboratory or field experiments are particularly encouraged.

The ultimate goal is to provide a platform for sharing methodologies and insights that can guide engineering practice in rock mechanics and geotechnical engineering.

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.

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.

This Special Issue addresses the critical need for robust numerical frameworks in the rapidly evolving field of bioengineering. While Artificial Intelligence (AI) and the Internet of Things (IoT) have significantly advanced biomedical diagnostics, the engineering challenges surrounding numerical stability, algorithmic innovation, and computational model efficiency remain underrepresented in current medical literature. This issue shifts the focus from purely clinical outcomes to the underlying engineering implementations.

We invite contributions that explore novel numerical algorithms for high-resolution medical imaging reconstruction, computational models for massive IoT-driven biomedical datasets, and the stability analysis of deep learning architectures in safety-critical diagnostic environments. By prioritizing optimization strategies and hardware-software co-design, this Special Issue seeks to provide a definitive platform for the engineering community to solve the technical bottlenecks hindering the real-time deployment of AI in modern medicine.

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.

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

The rapid development of Beyond-5G and 6G communication systems has stimulated extensive research in antenna engineering, metasurfaces, RF/microwave technologies, and intelligent wireless networks. These emerging systems face increasing requirements in terms of high data rates, massive connectivity, low latency, and efficient spectrum utilization, which in turn demand advanced numerical modeling, computational techniques, and simulation-driven design methodologies.

This Special Issue aims to highlight recent progress in the modeling, analysis, and design of antennas and next-generation wireless systems. It seeks contributions spanning computational electromagnetics, electromagnetic wave propagation, RF/microwave and metasurface structures, massive MIMO, RIS-assisted communication systems, integrated sensing and communication (ISAC), and beamforming techniques.

This issue welcomes contributions addressing both fundamental methods and practical implementations, including numerical simulations, experimental validations, and hardware-oriented developments relevant to future wireless technologies.

Topics of interest include, but are not limited to, the following:

Numerical modeling and simulation of antenna and metasurface structures

Computational electromagnetics and electromagnetic wave analysis

RF/microwave antenna design techniques

Metamaterial and reconfigurable metasurface systems

Wideband, multiband, and compact antenna designs

Electromagnetic propagation and channel modeling

Massive MIMO systems and beamforming techniques

RIS-assisted and intelligent wireless communication systems

Integrated sensing and communication (ISAC) frameworks

Numerical methods for wireless system analysis and optimization

Signal processing methods for wireless communications

AI-assisted modeling and system optimization

Simulation-based studies and experimental validation

Fractional calculus has grown into a field with many papers over the last decade and can be applied across physics, mathematical physics, the sciences, engineering, and many other fields. The fractional calculus attraction is due to the diversity of fractional operators. Many operators exist in fractional calculus, such as the Caputo derivative, Riemann-Liouville derivative, Conformable derivative, Caputo-Fabrizio derivative, Atangana-Baleanu derivative, and many other operators. In mathematics and physics literature, fractional operators can be used in modeling diffusion equations with reaction and without reaction terms, modeling electrical circuits, modeling fluid models, and finding solutions to fractional differential problems. Nowadays, many types of fractional differential equations exist, and methods to solve them have opened problems in fractional calculus. We can cite homotopy methods, Fourier and Laplace transform methods, predictor-corrector methods, implicit and explicit numerical schemes, etc. Therefore, this project will serve as an arena for modeling real-world problems using fractional operators. The second interest will be to propose the existence and uniqueness of fractional differential equations and the numerical and analytical methods for solving fractional differential equations. It is recommended to propose a method for solving fractional fluid models. Furthermore justification of the introduction of the fractional operator in modeling fluid will be more appreciated in the present collection. One of the main applications of fractional operators in mathematical physics is the modeling of diffusion processes. As mentioned in the literature, many diffusion processes exist as sub-diffusion, super-diffusion, hyper-diffusion, and ballistic diffusion. All the previously cited diffusion processes correspond to specific values of the fractional operators.

Analytical methods for fractional differential equations, fluid differential models, etc.

Numerical methods for fractional differential equations, fluid differential models, etc.

Modeling the chaotic systems with fractional operators.

Solution for the fractional diffusion equations with and without reaction terms.

Optimal control and stability analysis. Modeling fluids model using integer and fractional operators.

Applications of the fractional calculus in physics and mathematical physics.

Existence and uniqueness of the solution of the fractional differential equations.

The increasing demand for ultra deep oil and gas resources has driven in-depth research and significant progress in improving drilling speed and rock breaking, requiring the development of precise numerical simulation and calculation methods to achieve efficient design, analysis, and optimization.

This special issue of RIMNI International Journal of Numerical Methods for Engineering Calculation and Design aims to showcase cutting-edge research in computational technology to improve the performance, reliability, and economic feasibility of improving drilling speed and rock breaking.

We sincerely invite you to contribute to new numerical models, computational fluid thermodynamics and dynamics, multi physics field simulation, finite element analysis, and optimization design based on artificial intelligence methods for applications in speed up tools, key materials and components, rock breaking. The topics of interest include but are not limited to:

Numerical simulation and calculation method for characteristics of turbine tools, underwater energy enhancement tools and vibration suppression tools

Numerical simulation of erosion resistance characteristics and structural optimization for key components

Numerical simulation of erosion resistance characteristics and material optimization for key protective materials

Application of Artificial Intelligence Methods in tool and material optimization design

Application of computer vision and image processing methods in tool and material optimization design

This special issue aims to bridge the gap between theoretical advancements and practical applications, providing valuable insights into the future improvement of drilling speed and rock breaking through computational methods.

The construction of a new energy system has driven in-depth research and significant progress in underground space energy storage, requiring the development of precise numerical simulation and calculation methods to achieve efficient design, analysis, and optimization. This special issue of RIMNI International Journal of Numerical Methods for Engineering Calculation and Design aims to showcase cutting-edge research in computational technology to improve the performance, reliability, and economic feasibility of underground space energy storage.

We sincerely invite you to contribute to new numerical models, computational fluid thermodynamics and dynamics, multi physics field simulation, finite element analysis, and optimization design based on artificial intelligence methods for applications in natural gas storage, petroleum storage, underground storage of small molecule active gases, and compressed air energy storage in salt caverns. The topics of interest include but are not limited to:

Simulation of stability and sealing of underground space

Simulation of cavity construction and 3D visualization

Calculation and simulation of injection production

Calculation and simulation of energy storage wellbore integrity

Optimization of storage operation strategy

Application of artificial intelligence methods and computer vision in underground space energy storage

This special issue aims to bridge the gap between theoretical advancements and practical applications, providing valuable insights into the future improvement of underground space energy storage through computational methods.

Geotechnical and underground engineering encompasses a wide spectrum of critical infrastructure challenges, including tunneling, deep excavations, foundation systems, slope stability, underground storage, and subsurface resource extraction. The increasing complexity of modern geotechnical projects, combined with the inherent variability and uncertainty of geological materials, demands robust and sophisticated numerical simulation tools capable of capturing complex mechanical, hydraulic, thermal, and chemical behaviors of geomaterials.

This special issue aims to bring together cutting-edge research on numerical methods and their engineering applications in geotechnical and underground engineering. Topics of interest include, but are not limited to, the development and validation of advanced constitutive models for geomaterials, finite element and finite difference methods, discrete element methods, meshfree and particle-based approaches, as well as hybrid and multi-scale computational frameworks. Particular emphasis is placed on the simulation of coupled multi-physics processes, large deformation problems, geotechnical hazard assessment, and the integration of field monitoring data with numerical models.

Contributions bridging the gap between theoretical formulations and practical engineering applications are strongly encouraged. This special issue welcomes both original research articles and comprehensive review papers from academics, researchers, and practicing engineers worldwide, with the shared goal of advancing the state-of-the-art in computational geotechnics and underground engineering.

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

Deadline Date: 31 May 2026

The integration of soft computing, machine learning, and mathematical modeling is transforming real time automation across a wide range of engineering domains. With the rapid growth of the Internet of Things, edge computing, and cloud based systems, there is an increasing need for intelligent and adaptive solutions that can process large volumes of data and make decisions autonomously. These technologies empower systems to operate dynamically, respond to environmental changes, and optimize performance without manual intervention. Edge computing enables data processing close to the source, which reduces latency and supports real time responsiveness in applications such as healthcare monitoring, smart transportation, and home automation. Cloud computing complements edge devices by providing centralized learning, deeper analysis, and large scale storage. The combination of cloud and edge systems allows for the development of robust architectures that support distributed intelligence and real time simulation.

Soft computing techniques such as neural networks, fuzzy logic, and evolutionary algorithms are well suited to handle imprecise and nonlinear data. When integrated with mathematical modeling, these methods enable the construction of adaptive and predictive systems for engineering process optimization, intelligent control, and simulation. Additionally, automated learning pipelines help models evolve with real time data, improving accuracy, reliability, and efficiency.

This call for papers seeks contributions that explore the synergy of soft computing and mathematical modeling in real time automation. Areas of interest include intelligent edge AI, secure data driven decision making, predictive maintenance, and scalable engineering solutions. The objective is to foster research that advances smart and autonomous systems through computational intelligence and mathematical frameworks.

Fluid flow plays a vital role across a broad spectrum of industries as well as in environmental and biological systems, ranging from the development of predictive models for tsunamis and high-wave formation to the modeling of blood flow and tumor growth within the human body, and the enhancement of energy efficiency in solar power plants.

This Special Issue welcomes contributions addressing recent advances in both fundamental and applied aspects of fluid dynamics, with particular emphasis on energy, environmental, and industrial applications. In addition, this issue seeks innovative data-driven approaches for analyzing complex fluid flow phenomena.

This special issue aims to stimulate interdisciplinary collaboration, advance current scientific understanding, and promote innovative solutions that support sustainable, safe, and efficient operation of modern engineering systems.

Sampling is advantageous not only in the fields of Arts, Science, and Technology but is also useful in our daily lives. Due to sample variability, researchers apply scientific probability-based designs to select the sample. This reduces the risk of a distorted view of the population and allows statistically valid inferences to be made from the sample. The basic purpose of sampling is to obtain consistent, efficient, and unbiased estimates of the desired population parameters while considering cost, time, and effort savings. It is emphasized that a sample survey is usually less expensive than a census survey and the desired information may be obtained with a greater degree of accuracy. In sampling theory, emphasis is placed on the efficient use of suitable auxiliary information to improve the precision of estimates and reduce sampling errors.

In recent years, we have been moving toward comprehensive data analytics, a deeper understanding of data, and decision-making based on data. We have lots of data, but not always complete information. To understand the broader population and data behavior, researchers rely on estimation of population parameters using sample statistics and appropriate sampling strategies. Estimation of parameters like mean, median, mode, variance, skewness, and kurtosis is crucial in engineering for understanding system behaviour, performance evaluation, optimization, and predictive modelling.

The efficiency of estimators improves when they are closely related to the variable under study. For instance, historical system performance data can serve as auxiliary information to estimate future outcomes in engineering systems. Estimation is a fundamental problem in nearly every technical field, and simulation studies can enhance the robustness and applicability of these estimation methods.

This special issue aims to bring together researchers and practitioners working on estimation procedures using sampling and simulation theory to share novel ideas and findings, particularly focusing on engineering domains such as reliability analysis, structural modelling, system optimization, and industrial applications.

The Topics of interest for this special issue include, but are not limited to:

Theory of Estimation of Population Parameters:

•           Using auxiliary information

•           Using Auxiliary Attributes

•           Propagation Measurement Errors

•           Application of the above in various engineering fields.

Estimation Methods :

•           Using machine learning algorithm

•           Using linear and logistic regression

•           Big Data models

•           Application of the above in various engineering fields.  

The rapid evolution of large language models (LLMs) and generative AI has significantly advanced artificial intelligence, yet integrating these models into computational engineering and numerical design introduces critical challenges regarding security, integrity, and structural trustworthiness. For AI to be safely deployed in engineering environments—such as infrastructure design, fluid dynamics, and material science—addressing these issues is essential.

This Special Issue aims to explore the synergy between LLMs and computational engineering. It focuses on methodologies that enhance the robustness and reliability of AI-driven numerical methods and engineering simulations. We welcome contributions that discuss how LLMs can assist in automated engineering design, code generation for numerical solvers, and the interpretation of complex computational data. The issue seeks both theoretical and practical implementations that bridge the gap between advanced AI and dependable engineering calculations.

Topics of interest include, but are not limited to:

AI-augmented numerical methods and structural robustness

Trustworthy LLMs for automated engineering design and simulation

Security and integrity of AI-generated computational models

Explainable AI (XAI) for complex engineering decision-making

Pattern recognition and computer vision in structural health monitoring

Human–computer interaction for CAD and intelligent engineering software

Physics-informed machine learning for secure engineering applications

Privacy-preserving AI frameworks in collaborative engineering projects

The increasing demand for renewable energy has driven significant advancements in solar energy systems, necessitating the development of accurate simulation and numerical methods for their efficient design, analysis, and optimization. This special issue of RIMNI - International Journal of Numerical Methods for Calculation and Design in Engineering aims to showcase cutting-edge research on computational techniques that enhance the performance, reliability, and economic feasibility of solar energy technologies.

We invite contributions addressing novel numerical models, computational fluid dynamics (CFD), finite element analysis (FEA), artificial intelligence-based optimization, and multi-physics simulations applied to photovoltaic (PV) systems, concentrated solar power (CSP), hybrid solar technologies, and solar thermal applications. Topics of interest include, but are not limited to:

• Advanced numerical methods for solar radiation modeling and resource assessment
• Simulation-based optimization of photovoltaic and CSP systems
• Thermal performance modeling of solar collectors and storage systems
• Computational approaches for hybrid solar energy integration
• AI-driven and machine learning models for solar energy forecasting and control
• Numerical simulations for material and structural analysis in solar energy components

This special issue aims to bridge the gap between theoretical advancements and real-world applications, providing valuable insights into the future of solar energy system design through computational methodologies.

With the accelerating pace of urbanization and the large-scale construction of major infrastructure projects, engineering infrastructure systems are evolving toward greater scale, complexity, and multifunctionality. During long-term service, these structures are inevitably exposed to various complex environmental actions, including earthquakes, fires, explosions, impacts, fatigue, corrosion, extreme weather, and coupled multi-hazard effects, making issues related to structural safety, stability, and resilience increasingly prominent. Catastrophic structural failures are typically characterized by suddenness, strong nonlinearity, multiscale interactions, and multiphysics coupling. As a result, traditional experimental and theoretical approaches face considerable challenges in revealing failure mechanisms, reproducing full-process structural responses, and predicting structural performance under complex operating conditions.

Recent advances in numerical simulation, high-performance computing, digital twins, data-driven approaches, and artificial intelligence technologies have provided new theoretical foundations and technical pathways for investigating catastrophic failures and evaluating the safety performance of engineering infrastructure. In particular, intelligent algorithms such as machine learning, deep learning, generative artificial intelligence, and physics-informed neural networks (PINNs) are promoting the transformation of conventional structural analysis methods from empirical-driven approaches toward data–physics fusion paradigms. These developments offer important support for high-precision simulation, real-time prediction, and intelligent decision-making in complex structural failure scenarios.

This special issue focuses on recent advances in simulation methods and intelligent algorithms for catastrophic failures in engineering infrastructure. We warmly invite researchers and practitioners to contribute original research articles, review papers, and case studies. Topics of interest include, but are not limited to, the following:

a) Simulation and analysis of catastrophic mechanisms and failure modes in engineering structures

b) Multi-Scale and Multi-Physics coupled simulation methods for structural catastrophe

c) Artificial intelligence-based structural damage identification and safety assessment

d) Integrated modeling based on data-driven and physics-driven approaches

e) Digital twin technology and intelligent operation and maintenance of engineering infrastructure

f) Probabilistic risk analysis and structural resilience assessment

g) Intelligent optimization design and reliability analysis of engineering structures

h) Simulation and analysis of catastrophic behavior in advanced materials and composite structures

In modern engineering, uncertainty is an inherent characteristic of product design and manufacturing processes. Sources of uncertainty—ranging from material variability and geometric tolerances to operational conditions and human-induced factors—can significantly affect product performance, quality, and reliability. Thus, how to accurately simulate, model and quantify these uncertainties to ensure high reliability and long service life of products is a research hotspot in the field of engineering design. Uncertainty quantification in product design and manufacturing is a powerful tool to tackle the above challenges.

Uncertainty Quantification (UQ) provides a systematic framework to model, propagate, and mitigate these uncertainties, ensuring more robust and reliable product development. In recent years, the integration of UQ with advanced computational tools and surrogate models has enabled engineers to tackle complex, high-dimensional problems that were previously intractable.

This special issue would aim to establish an academic exchange platform between experts and scholars, also, to establish a common understanding about the state of the field and draw a road map on where the research is heading, highlight the issues and discuss the possible solutions, and provide the data, models, and methods necessary to perform uncertainty quantification in product design and manufacturing. Potential topics include, but are not limited to:

(1)Computer simulation and mathematical modeling in product design and manufacturing

(2)Theory and method of uncertainty quantification in product design and manufacturing

(3)Design and manufacturing process parameters optimization based on computer simulation

(4)Reliability analysis and reliability design optimization based on surrogate model

(5)Design methods and applications for high-reliability and long-life products

Smart cities aim to enhance the quality of urban life by efficiently managing resources, infrastructure, and services through advanced technologies. With rapid urbanization, cities face complex challenges related to energy consumption, transportation, public safety, healthcare, environmental sustainability, and governance. Soft computing, machine learning, artificial intelligence, and data-driven approaches have emerged as powerful tools to address these challenges by enabling intelligent decision-making, predictive analytics, automation, and real-time monitoring. These techniques can effectively handle uncertainty, large-scale data, and complex urban systems, making them essential for the development of smart and sustainable cities.

The aim of this Special Issue is to provide a comprehensive platform for researchers and practitioners to present recent advances, innovative methodologies, and real-world applications of intelligent and data-driven technologies in smart city environments. The scope includes theoretical developments, practical implementations, and case studies that demonstrate how these approaches improve urban efficiency, sustainability, resilience, and citizen well-being. Suggested themes include, but are not limited to: smart transportation and traffic management, intelligent energy systems and smart grids, urban data analytics, IoT-enabled smart city applications, AI-based public safety and surveillance, healthcare and smart living solutions, environmental monitoring, predictive maintenance of urban infrastructure, and ethical, privacy, and security issues in smart city data analytics.

Fractional calculus has emerged as a powerful mathematical framework for describing engineering systems characterized by memory effects, hereditary properties, anomalous diffusion, viscoelasticity, nonlocal transport, and multiscale dynamics. In many practical applications, classical integer-order models are insufficient because the current state of a system depends not only on local rates of change but also on its historical evolution.

This Special Issue aims to collect recent advances in fractional-order modelling, analysis, numerical computation, and engineering applications, with particular emphasis on variable-order fractional operators, fractional partial differential equations, nonlinear fractional dynamics, and robust computational methods.

Topics of interest include, but are not limited to:

Caputo, Riemann–Liouville, Hilfer, Caputo–Fabrizio, Atangana–Baleanu, distributed-order, and variable-order fractional models;

Fractional partial differential equations (PDEs) in diffusion, wave propagation, fluid mechanics, heat transfer, porous media, control systems, signal processing, biomechanics, and materials science;

Stability, bifurcation, and dynamical analysis of fractional systems;

Spectral, finite difference, finite element, meshless, and iterative numerical methods for fractional problems;

Inverse problems, parameter identification, and optimization techniques in fractional models;

Numerical validation and computational implementation of fractional engineering systems;

Fractional modelling and simulation for engineering design and multiscale applications.

The aim of this Special Issue is to provide a focused platform for high-quality contributions that bridge rigorous developments in fractional calculus with practical engineering applications, numerical simulation, computational design, and optimization.

Wave propagation (elastic, acoustic, electromagnetic waves) is widespread in civil/mechanical engineering, wireless communication, remote sensing, and meteorology. In practical scenarios, it occurs in complex environments—e.g., electromagnetic waves scattered/absorbed by particulate media, and underwater acoustic waves reflected by sea surfaces/seabeds. It involves multi-physics coupling (e.g., ultrasound-induced heat in porous media, elastic waves triggering electric fields in piezoelectric materials) and requires multi-scale analysis (e.g., integrating particle physics, electromagnetics for light transmission). Due to inherent complexity, open questions remain in modeling, simulation, and experiments. This Research Topic collects original/review articles on cutting-edge progress in these areas, welcoming physical modeling combined with data-driven techniques, and research on advanced materials/smart structures for wave propagation.

Potential Topics (Include but Are Not Limited To)

- Acoustic-solid coupling interactions in complex shallow and deep underwater environments

- Electromagnetic wave scattering and propagation mechanisms in rough surface and dense particulate media

- Statistical homogenization methods and multi-scale bridging theories for micro-macro wave propagation analysis

- Theoretical modeling frameworks and advanced experimental techniques for characterizing optical properties of anisotropic and heterogeneous media

- Development of novel numerical simulation methods integrated with machine learning for multi-physics wave propagation analysis

Numerical methods have emerged as comprehensive tools for analyzing complex multi-physical field problems, particularly as computing power has increased significantly in recent years. Wood and wood products are among the oldest construction and manufacturing materials in human history; thanks to their natural, sustainable, and aesthetic properties, they remain important today. Traditionally, wood construction and furniture design have relied heavily on craftsmanship, experience, and intuitive approaches gained from past applications. However, today's engineering approach requires the scientific study of material behavior and the evaluation of performance criteria such as load-bearing capacity, deformation, and stability in light of mathematical theories. Relying solely on experience for strength design, particularly in furniture and wood structural elements, can lead to significant limitations in terms of safety, durability, and material efficiency. In contrast, mathematical modeling and numerical analysis methods enable the development of balanced, reliable, and optimized solutions that meet both aesthetic expectations and technical requirements.

The primary objective of this special issue is to examine the role of numerical methods in the design, analysis, and production processes of wood materials and wood products within a scientific framework and to bring together current research in this field. The natural, heterogeneous, and anisotropic structure of wood requires special approaches in terms of engineering calculations and performance predictions. In this regard, mathematical modeling, numerical analysis, and the use of computer-aided engineering tools contribute significantly to the development of safe, durable, economical, and aesthetically balanced designs for wood structural elements and wood products, especially furniture. This special issue aims to go beyond traditional experience-based approaches and promote the widespread use of scientific and numerical methods.

The scope of this special issue includes topics such as the numerical modeling of the mechanical and physical behavior of wood material, finite element analysis applications, optimization techniques, computer-aided design and manufacturing (CAD/CAM) approaches, structural performance and strength analyses, numerical investigation of fasteners, sustainable material use, and material efficiency. In addition, interdisciplinary studies aiming to balance aesthetic requirements with engineering criteria in the design of wood and wood-based products are also within the scope of this special issue. Highlighting the benefits offered by numerical methods, such as time savings, cost advantages, and design accuracy, and evaluating the applicability of these approaches on a broader scale within the industry are among the primary objectives of this special issue.

Suggested themes:

Numerical Analysis of Wood and Wood-Based Composite Materials

Finite Element Analysis of Wood Structures and Furniture Components

Strength and Performance-Based Wood Product Design

Numerical Analysis and Optimization of Fasteners

Optimization Techniques and Material Efficiency

CAD/CAM and Digital Manufacturing Technologies

Multi-Scale and Multi-Physics Numerical Modeling

Experimental Validation and Numerical Model Calibration

Artificial Intelligence and Data-Driven Numerical Methods

Sustainability and Life Cycle Analysis

We invite researchers and practitioners to submit manuscripts to this Special Issue of RIMNI – International Journal of Numerical Methods for Calculation and Design in Engineering, focused on numerical methods, computational models, and simulation-driven design for hybrid ambient energy harvesting and ultra-low-power power management.

Hybrid harvesters combine multiple ambient sources—mechanical (piezoelectric/triboelectric), thermal, light, and radio frequency—to enable long-lived self-powered systems. Achieving robust performance across variable environments requires accurate multi-physics modeling, efficient numerical solvers, and system-level co-simulation that couples transducers, interfaces, and micro-scale storage. This Special Issue aims to bridge theoretical advances in numerical methods with practical engineering design, emphasizing verification/validation, standardized benchmarking, and computationally efficient workflows suitable for real operating conditions.

We welcome Research Articles and Reviews addressing method development and/or high-quality applications with clear numerical contributions.

Topics of interest (non-exhaustive)

• Multi-physics formulations and FEM/BEM/FDTD for coupled-field harvesters (electromechanical, thermo-electrical, electrostatic/contact, Maxwell-based EM)

• Nonlinear dynamics, contact/friction, and charge-transfer modeling for triboelectric systems; multi-scale or homogenized models

• Thermal transport and conjugate heat transfer (including CFD coupling where relevant) for thermoelectric/pyroelectric harvesters

• Electromagnetic and circuit co-design for RF rectennas: matching networks, broadband front-ends, field–circuit co-simulation

• Indoor PV modeling under low-lux conditions: spectral effects, partial shading, stochastic illumination, degradation-aware models

• System-level co-simulation: transducer–interface–storage coupling, SPICE/FEM co-simulation, switching/averaged power electronics models

• Numerical optimization and inverse design: topology/shape/parameter optimization, sensitivity analysis, adjoint methods, surrogate modeling

• MPPT, energy routing, and control algorithms with numerical analysis of stability/robustness and implementation constraints

• Reduced-order modeling (ROM) and model reduction for fast parametric sweeps, digital twins, and embedded deployment

• Uncertainty quantification and stochastic modeling of ambient inputs, material variability, and reliability/lifetime prediction

• Verification, validation, and benchmarking: reproducible workflows, reference problems, standardized metrics, experimental–numerical correlation

• High-performance computing and solver advances: scalable linear/nonlinear solvers, preconditioning, parallel efficiency, workflow automation

This collection gathers the plenary lectures presented at PARTICLES 2015, held on 28-30 September 2015, Barcelona, Spain.

PARTICLES 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.

Conference organized by CIMNE Congress Bureau.

The next edition of this conference will be held in Barcelona, Spain, 28 - 30 October 2019 (https://congress.cimne.com/particles2019/frontal/default.asp)

COUPLED VI 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.

Conference organized by CIMNE Congress Bureau.

The next edition of this conference (Coupled 2021) will be held in Chia Laguna, South Sardinia, Italy, 13-16 June 2021 (https://congress.cimne.com/Coupled2021/frontal/default.asp) 

This collection gathers the plenary lectures presented at COMPLASS XIII, held on 1-3 September 2015 at Barcelona, Spain.

COMPLAS XIII 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.

Conference organized by CIMNE Congress Bureau.

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

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.

Conference organized by CIMNE Congress Bureau.

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

The objective of Marine 2025 is to be a meeting place for researchers developing computational methods and scientists and engineers focusing on challenging applications in marine engineering.

ISSN: 2938-8961