Deep Learning in Computational Mechanics - An Introductory Course (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 977
Release date:	August 2022
First published:	2021
Authors:	Stefan Kollmannsberger • Davide D'Angella • Moritz Jokeit • Leon Herrmann
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	104
Edition:	1st ed. 2021
ISBN-13:	978-3-03-076589-7
Categories:	Books > Professional & Technical > Mechanical engineering & materials > Materials science > Engineering thermodynamics Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	3-03-076589-X
Barcode:	9783030765897

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