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Deep learning in computational mecha...

Kollmannsberger, Stefan.

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Deep learning in computational mechanics = an introductory course /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning in computational mechanics/ by Stefan Kollmannsberger ... [et al.].
Reminder of title:	an introductory course /
other author:	Kollmannsberger, Stefan.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	vi, 104 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Fundamental Concepts of Machine Learning -- Neural Networks -- Machine Learning in Physics and Engineering -- Physics-informed Neural Networks -- Deep Energy Method.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-76587-3
ISBN:	9783030765873

Deep learning in computational mechanics = an introductory course /
Deep learning in computational mechanicsan introductory course /[electronic resource] :by Stefan Kollmannsberger ... [et al.]. - Cham :Springer International Publishing :2021. - vi, 104 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.9771860-949X ;. - Studies in computational intelligence ;v.977..

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:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Deep learning in computational mechanics = an introductory course /
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245 0 0 $a Deep learning in computational mechanics $h [electronic resource] : $b an introductory course / $c by Stefan Kollmannsberger ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a vi, 104 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.977
505 0 $a Introduction -- Fundamental Concepts of Machine Learning -- Neural Networks -- Machine Learning in Physics and Engineering -- Physics-informed Neural Networks -- Deep Energy Method.
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.
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650 2 4 $a Engineering Thermodynamics, Heat and Mass Transfer. $3 1002079
700 1 $a Kollmannsberger, Stefan. $3 3514514
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830 0 $a Studies in computational intelligence ; $v v.977. $3 3514515
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950 $a Intelligent Technologies and Robotics (SpringerNature-42732)