Deadline Date: 30 April 2026
1) The issue introduction includes the background and the importance of this research area.
The fusion of Artificial Intelligence (AI) with engineering simulation and numerical analysis is transforming the industrial landscape, forming a key pillar of Industry 4.0. Traditional numerical methods such as finite element analysis (FEA), computational fluid dynamics (CFD), and structural modeling are increasingly being enhanced through AI techniques—ranging from machine learning and deep neural networks to reinforcement learning and computer vision. This integration offers significant advantages: accelerated simulations, improved predictive accuracy, and reduced computational cost.
AI-driven models allow for automated parameter optimization, adaptive mesh refinement, and real-time predictive simulations, making engineering processes smarter, faster, and more resilient. In industrial applications, these AI-powered tools enable better product design, lower material waste, and reduced time-to-market. Additionally, when integrated with cyber-physical systems and digital twins, they provide continuous monitoring and predictive maintenance capabilities, minimizing downtime and enhancing reliability.
Despite these advancements, critical challenges remain. High computational complexity, lack of standardized integration frameworks, and concerns around interpretability and safety in mission-critical applications limit wider adoption. Addressing these issues is essential for realizing the full potential of AI in engineering.
This special issue aims to explore cutting-edge research and developments in AI-augmented numerical methods, showcasing how they can unlock the next level of intelligent, data-driven engineering for Industry 4.0.
2) The aim and scope of the Special Issue shall be highlighted.
The aim of this Special Issue is to investigate and promote the integration of Artificial Intelligence (AI) with numerical analysis and engineering simulation in support of Industry 4.0 applications. As industries move toward smart, autonomous, and data-driven operations, AI offers powerful tools to enhance traditional simulation methods such as finite element analysis (FEA), computational fluid dynamics (CFD), and structural modeling. This issue seeks to highlight innovative AI methodologies—including machine learning, deep learning, reinforcement learning, and evolutionary computing—that improve model accuracy, reduce computational time, and enable real-time predictive simulations. The scope encompasses AI-driven simulation techniques for manufacturing, optimization, intelligent system design, digital twins, uncertainty quantification, and cyber-physical integration. By bridging theory and practice, the Special Issue aims to provide a forum for researchers and industry professionals to share advanced techniques and applications that redefine computational engineering for the next generation of intelligent, adaptive industrial systems.
3) Suggested themes shall be listed.
Evolutionary Algorithms in Engineering Simulation and Optimization for Industry 4.0
Quantum-Inspired AI Techniques for Numerical Computation and Engineering Analysis
Fuzzy Logic Systems for Adaptive Numerical Modeling in Engineering Applications
Generative Adversarial Networks for Engineering Simulation and Data-Driven Numerical Analysis
Bayesian Learning Models for Uncertainty Quantification in Numerical Methods and Simulation
Physics-Informed Neural Networks for Engineering Simulation and Computational Analysis
Swarm Intelligence Approaches to Numerical Optimization in Smart Manufacturing Systems
Autonomous Multi-Agent Systems for Engineering Simulation and Predictive Numerical Analysis
High-Performance Computing with AI for Large-Scale Engineering Simulations
Probabilistic Graph Models for Numerical Analysis and Computational Engineering in Industry 4.0
Metaheuristic AI Algorithms for Engineering Simulation and Complex Numerical Methods
Cognitive Computing for Intelligent Numerical Modeling and Engineering Simulation in Industry 4.0
Editorial board
