Buch, Englisch, Band 667, 232 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 533 g
Buch, Englisch, Band 667, 232 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 533 g
Reihe: Studies in Systems, Decision and Control
ISBN: 978-3-032-20306-9
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik Mathematik Mathematische Analysis
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
Weitere Infos & Material
Introduction.- Machine Learning Concepts.- Partial Differential Equations in Engineering.- Machine Learning based solutions of PDEs.- Surrogate Models based on Machine Learning.- Neural Operators.- Conclusions and Future Directions.
