Deep learning in computational mechanics : an introductory course /
Saved in:
| Author / Creator: | Kollmannsberger, Stefan, author. |
|---|---|
| Imprint: | Cham : Springer, [2021] ©2021 |
| Description: | 1 online resource (108 pages) : illustrations (some color). |
| Language: | English |
| Series: | Studies in computational intelligence ; volume 977 Studies in computational intelligence ; v. 977. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/12631124 |
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| 100 | 1 | |a Kollmannsberger, Stefan, |e author. | |
| 245 | 1 | 0 | |a Deep learning in computational mechanics : |b an introductory course / |c Stefan Kollmannsberger, Davide D'Angella, Moritz Jokeit, Leon Herrmann. |
| 264 | 1 | |a Cham : |b Springer, |c [2021] | |
| 264 | 4 | |c ©2021 | |
| 300 | |a 1 online resource (108 pages) : |b illustrations (some color). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Studies in computational intelligence ; |v volume 977 | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. . | ||
| 505 | 0 | |a Introduction -- Fundamental Concepts of Machine Learning -- Neural Networks -- Machine Learning in Physics and Engineering -- Physics-informed Neural Networks -- Deep Energy Method. | |
| 588 | |a Description based upon print version of record. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a D'Angella, Davide, |e author. | |
| 700 | 1 | |a Jokeit, Moritz, |e author. | |
| 700 | 1 | |a Herrmann, Léon, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Kollmannsberger, Stefan |t Deep Learning in Computational Mechanics |d Cham : Springer International Publishing AG,c2021 |z 9783030765866 |
| 830 | 0 | |a Studies in computational intelligence ; |v v. 977. |0 http://id.loc.gov/authorities/names/no2005104439 | |
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| 928 | |t Library of Congress classification |a Q325.5 .D44 2021 |l Online |c UC-FullText |u https://link.springer.com/10.1007/978-3-030-76587-3 |z Springer Nature |g ebooks |i 12654066 | ||