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.

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. 

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

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.

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.

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.

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.

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 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.

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.

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