Deep Learning in Computational Mechanics : An Introductory Course
2021 ed.
Book Details
Format
Hardback or Cased Book
Book Series
Studies in Computational Intelligence
ISBN-10
3030765865
ISBN-13
9783030765866
Edition
2021 ed.
Publisher
Springer Nature Switzerland AG
Imprint
Springer Nature Switzerland AG
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Aug 6th, 2021
Print length
104 Pages
Weight
302 grams
Dimensions
24.40 x 16.30 x 1.30 cms
Ksh 13,500.00
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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.
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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