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Deep Learning in Computational Mechanics

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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.

Availability
Inventory Code Barcode Call Number Location Status
1908000795EB0001375006.31 Kal dCentral Library (OPAC)Available
Detail Information
Series Title
Studies in Computational Intelligence
Call Number
006.31 Kal d
Publisher
Switzerland : Springer Cham.,
Collation
vi, 104p.:lll
Language
English
ISBN/ISSN
978-3-030-76587-3
Classification
006.31
Content Type
Ebook
Media Type
-
Carrier Type
online resource
Edition
1
Subject(s)
Machine Learning
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

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