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Deep Learning in Computational Mecha...
~
D'Angella, Davide.
Deep Learning in Computational Mechanics = An Introductory Course /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Deep Learning in Computational Mechanics/ by Stefan Kollmannsberger, Davide D'Angella, Moritz Jokeit, Leon Herrmann.
其他題名:
An Introductory Course /
作者:
Kollmannsberger, Stefan.
其他作者:
Herrmann, Leon.
面頁冊數:
VI, 104 p. 41 illus., 22 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Engineering Thermodynamics, Heat and Mass Transfer. -
電子資源:
https://doi.org/10.1007/978-3-030-76587-3
ISBN:
9783030765873
Deep Learning in Computational Mechanics = An Introductory Course /
Kollmannsberger, Stefan.
Deep Learning in Computational Mechanics
An Introductory Course /[electronic resource] :by Stefan Kollmannsberger, Davide D'Angella, Moritz Jokeit, Leon Herrmann. - 1st ed. 2021. - VI, 104 p. 41 illus., 22 illus. in color.online resource. - Studies in Computational Intelligence,9771860-9503 ;. - Studies in Computational Intelligence,564.
Introduction -- Fundamental Concepts of Machine Learning -- Neural Networks -- Machine Learning in Physics and Engineering -- Physics-informed Neural Networks -- Deep Energy Method.
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. .
ISBN: 9783030765873
Standard No.: 10.1007/978-3-030-76587-3doiSubjects--Topical Terms:
769147
Engineering Thermodynamics, Heat and Mass Transfer.
LC Class. No.: Q342
Dewey Class. No.: 006.3
Deep Learning in Computational Mechanics = An Introductory Course /
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