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Deep learning in computational mechanicsan introductory course /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning in computational mechanicsby Leon Herrmann ... [et al.].
Reminder of title:	an introductory course /
other author:	Herrmann, Leon.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xxvi, 475 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-031-89529-6
ISBN:	9783031895296$q(electronic bk.)

Deep learning in computational mechanicsan introductory course /
Deep learning in computational mechanicsan introductory course /[electronic resource] :by Leon Herrmann ... [et al.]. - Second edition. - Cham :Springer Nature Switzerland :2025. - xxvi, 475 p. :ill., digital ;24 cm.

Computational Mechanics Meets Arti?cial Intelligence -- Neural Networks -- Machine Learning in Computational Mechanics -- Methodological Overview of Deep Learning in Computational Mechanics -- Index.

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.

ISBN: 9783031895296$q(electronic bk.)

Standard No.: 10.1007/978-3-031-89529-6doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Deep learning in computational mechanicsan introductory course /
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505 0 $a Computational Mechanics Meets Arti?cial Intelligence -- Neural Networks -- Machine Learning in Computational Mechanics -- Methodological Overview of Deep Learning in Computational Mechanics -- Index.
520 $a 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.
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