Scientific Machine Learning with Engineering Applications
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Beschreibung
This book equips readers with a rigorous and practical framework for solving complex engineering problems directly from governing equations using modern machine learning techniques. It bridges established principles from mechanics, numerical analysis, and scientific computing with emerging physics-based learning approaches, enabling reliable modeling, simulation, optimization, and inverse analysis beyond purely data-driven methods. A distinctive feature is its critical comparison of machine learning-based solvers with classical techniques such as the finite element method, isogeometric analysis, and meshfree methods, highlighting strengths, limitations, and domains of applicability. The scope ranges from foundational concepts to advanced engineering applications, supported by worked examples, reproducible code, and extensive references. The book is intended for graduate students, researchers, and practitioners in engineering, applied mathematics, and computational sciences who seek a principled entry point and a state-of-the-art reference for physics-based machine learning in modeling and simulation.
This book equips readers with a rigorous and practical framework for solving complex engineering problems directly from governing equations using modern machine learning techniques. It bridges established principles from mechanics, numerical analysis, and scientific computing with emerging physics-based learning approaches, enabling reliable modeling, simulation, optimization, and inverse analysis beyond purely data-driven methods. A distinctive feature is its critical comparison of machine learning-based solvers with classical techniques such as the finite element method, isogeometric analysis, and meshfree methods, highlighting strengths, limitations, and domains of applicability. The scope ranges from foundational concepts to advanced engineering applications, supported by worked examples, reproducible code, and extensive references. The book is intended for graduate students, researchers, and practitioners in engineering, applied mathematics, and computational sciences who seek a principled entry point and a state-of-the-art reference for physics-based machine learning in modeling and simulation.
One of the first books on machine learning based solutions of partial differential equations Covers topics related to surrogate models, optimization and inverse analysis Complemented with computer code, examples and a documentation useful for researchers who want to work in this area Provides an in-depth discussion and comparison to other ‘classical’ methods such as FEM, IGA and meshless methods
Autor*in
Timon Rabczuk
Themen in »Scientific Machine Learning with Engineering Applications«
Physics Informed Neural Networks Partial Differential Equations Computational Mechanics Modeling and Simulation