Scientific Machine Learning with Engineering Applications: 667 - Rilegato

Rabczuk, Timon; Anitescu, Cosmin; Goswami, Somdatta; Zhuang, Xiaoying; Wang, Yizheng

Rilegato

ISBN 10: 3032203066 ISBN 13: 9783032203069

Casa editrice: Springer-Verlag GmbH, 2026

Riassunto

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.

Su questo libro

Informazioni sull'autore

Timon Rabczuk is chaired professor of Computational Mechanics at the Bauhaus Universität Weimar, Germany. He has published more than 600 SCI papers, mainly about computational methods for the solution of partial differential equations. He is the editor-in-chief of CMC-Computers, Materials and Continua, an associated editor of International Journal of Impact Engineering and Applied Physics A, and an executive editor of FSCE-Frontiers of Structural and Civil Engineering. He was listed as one of ISI Highly Cited Researchers in Computer Science and Engineering from 2014 up to now. Huilong Ren is currently a research fellow at the Institut für Photonik of Leibniz Universität Hannover, Germany. He obtained his Ph.D. degree by the thesis "Dual-horizon peridynamics and nonlocal operator method" at Institut für Strukturmechanik (ISM) of Bauhaus-Universität Weimar. He is interested in nonlocality-based methods (peridynamics, nonlocal operator method), numerical methods (FEM, IGA, meshless methods), and automatic code generation for numerical solutions of partial differential equations.

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer-Verlag GmbH
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 3032203066
• ISBN 13: 9783032203069
• Rilegatura: Copertina rigida
• Numero di pagine: 243
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania