UID:
almahu_9949139688002882
Format:
VI, 104 p. 41 illus., 22 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9783030765873
Series Statement:
Studies in Computational Intelligence, 977
Content:
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning's fundamental concepts before neural networks are explained thoroughly. It then provides an overview of current topics in physics and engineering, setting the stage for the book's main topics: physics-informed neural networks and the deep energy method. The idea of the book is to provide the basic concepts in a mathematically sound manner and yet to stay as simple as possible. To achieve this goal, mostly one-dimensional examples are investigated, such as approximating functions by neural networks or the simulation of the temperature's evolution in a one-dimensional bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning framework for python. .
Note:
Introduction -- Fundamental Concepts of Machine Learning -- Neural Networks -- Machine Learning in Physics and Engineering -- Physics-informed Neural Networks -- Deep Energy Method.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030765866
Additional Edition:
Printed edition: ISBN 9783030765880
Additional Edition:
Printed edition: ISBN 9783030765897
Language:
English
DOI:
10.1007/978-3-030-76587-3
URL:
https://doi.org/10.1007/978-3-030-76587-3
