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Deep learning in computational mechanics : an introductory course / Leon Herrmann, Moritz Jokeit, Oliver Weeger, Stefan Kollmannsberger

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Cham : Springer, 2025Edition: Second editionDescription: 1 Online-Ressource (xxvi, 475 Seiten) : Illustrationen, DiagrammeISBN:
  • 9783031895296
Subject(s): Additional physical formats: 9783031895289 | Erscheint auch als: Deep Learning in Computational Mechanics. Druck-Ausgabe Second Edition 2025. Cham : Springer International Publishing AG, 2025. 475 SeitenDDC classification:
  • 006.3 23
DOI: DOI: 10.1007/978-3-031-89529-6Online resources: Summary: This book provides a first course without requiring prerequisite knowledge. Fundamental concepts of machine learning are introduced before explaining neural networks. With this knowledge, prominent topics in deep learning for simulation are explored. These include surrogate modeling, physics-informed neural networks, generative artificial intelligence, Hamiltonian/Lagrangian neural networks, input convex neural networks, and more general machine learning techniques. The idea of the book is to provide basic concepts as simple as possible but in a mathematically sound manner. Starting point are one-dimensional examples including elasticity, plasticity, heat evolution, or wave propagation. The concepts are then expanded to state-of-the-art applications in material modeling, generative artificial intelligence, topology optimization, defect detection, and inverse problemsPPN: PPN: 1943461031Package identifier: Produktsigel: ZDB-2-SXIT | ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT
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