Multiphysics Simulation and AI in Computational Engineering
Physics, Intelligence, and Practical Applications
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Beschreibung
This book provides a comprehensive introduction to multiphysics simulation, artificial intelligence (AI), and their integration in modern computational engineering. It presents the mathematical and computational foundations of physics-based simulation and demonstrates how AI can be incorporated into advanced engineering analysis to improve modeling, prediction, and computational efficiency.
The book covers major multiphysics problems, including fluid–structure interaction (FSI), thermal–mechanical coupling, and other coupled physical phenomena, together with numerical techniques such as the finite element method (FEM), finite difference method (FDM), and meshless approaches. These methods are presented in the context of solving complex problems across mechanical, materials, and biomedical engineering.
Building from the fundamentals of neural networks, the book introduces a broad range of modern AI methods, including Physics-Informed Neural Networks (PINNs), Convolutional Neural Networks (CNNs), Bayesian Neural Networks (BNNs), Generative Adversarial Networks (GANs), and Transformers. Particular emphasis is placed on the interaction between physics-based models and data-driven methods and on their practical use in computational engineering.
The book also addresses important aspects of reliable computational modeling, including verification, validation, and uncertainty quantification, and illustrates the presented concepts through practical engineering applications and case studies. By combining mathematical foundations, numerical simulation, multiphysics modeling, and modern AI techniques, the book provides undergraduate and graduate students, researchers, and engineering professionals with a unified framework for understanding and applying AI-enabled computational methods to real-world engineering problems.
This book provides a comprehensive introduction to multiphysics simulation, artificial intelligence (AI), and their integration in modern computational engineering. It presents the mathematical and computational foundations of physics-based simulation and demonstrates how AI can be incorporated into advanced engineering analysis to improve modeling, prediction, and computational efficiency.
The book covers major multiphysics problems, including fluid–structure interaction (FSI), thermal–mechanical coupling, and other coupled physical phenomena, together with numerical techniques such as the finite element method (FEM), finite difference method (FDM), and meshless approaches. These methods are presented in the context of solving complex problems across mechanical, materials, and biomedical engineering.
Building from the fundamentals of neural networks, the book introduces a broad range of modern AI methods, including Physics-Informed Neural Networks (PINNs), Convolutional Neural Networks (CNNs), Bayesian Neural Networks (BNNs), Generative Adversarial Networks (GANs), and Transformers. Particular emphasis is placed on the interaction between physics-based models and data-driven methods and on their practical use in computational engineering.
The book also addresses important aspects of reliable computational modeling, including verification, validation, and uncertainty quantification, and illustrates the presented concepts through practical engineering applications and case studies. By combining mathematical foundations, numerical simulation, multiphysics modeling, and modern AI techniques, the book provides undergraduate and graduate students, researchers, and engineering professionals with a unified framework for understanding and applying AI-enabled computational methods to real-world engineering problems.
Enhances learning through case studies focusing on actual engineering problems relevant in academia and industry Teaches advanced AI techniques for engineering, bridging ai and physics-based simulations Equips readers with physics-based computational tools featuring both theoretical explanations and hands-on examples
Autor*in
Hamed Hosseinzadeh
Themen in »Multiphysics Simulation and AI in Computational Engineering«
Multiphysics simulation AI in computational engineering Neural networks in engineering Physics-informed neural networks (PINNs) Simulation optimization with AI AI in material design AI-enhanced predictive maintenance Fluid-structure interaction (FSI) with AI AI and biomechanics simulation Thermal-mechanical coupling in AI