Deep Learning in Computational Mechanics : An Introductory Course / / by Leon Herrmann, Moritz Jokeit, Oliver Weeger, Stefan Kollmannsberger

• Autore: Herrmann Léon
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
• Edizione: 2nd ed. 2025.
• Descrizione fisica: 1 online resource (690 pages)
• Disciplina: 620.1
• Soggetto topico: Computational intelligence; Machine learning; Thermodynamics; Heat engineering; Heat - Transmission; Mass transfer; Computational Intelligence; Machine Learning; Engineering Thermodynamics, Heat and Mass Transfer
• Nota di contenuto: Computational Mechanics Meets Artificial Intelligence -- Neural Networks -- Machine Learning in Computational Mechanics -- Methodological Overview of Deep Learning in Computational Mechanics -- Index.
• Sommario/riassunto: 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 problems.
• Titolo autorizzato: Deep Learning in Computational Mechanics
• ISBN: 3-031-89529-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911047824203321
• Lo trovi qui: Univ. Federico II
• Serie: Intelligent Technologies and Robotics Series