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商品詳細
Deep Learning in Computational Mechanics: An Introductory Course. 2021 ed.
・ISBN 978-3-030-76589-7 paper EUR 59.99
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| 著者・編者 | Kollmannsberger, Stefan / D'Angella, Davide / Jokeit, Moritz / Herrmann, Leon, |
|---|---|
| シリーズ | (Studies in Computational Intelligence) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2022 |
| ページ数 | 104 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-60547＞ |
解説
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.