Deep Learning in Computational Mechanics : An Introductory Course / by Stefan Kollmannsberger, Davide D'Angella, Moritz Jokeit, Leon Herrmann.

Format:

Book

Author/Creator:

Kollmannsberger, Stefan, author.

Series:

Studies in Computational Intelligence, 1860-9503 ; 977

Language:

English

Subjects (All):

Computational intelligence.

Machine learning.

Thermodynamics.

Heat engineering.

Heat--Transmission.

Heat.

Mass transfer.

Computational Intelligence.

Machine Learning.

Engineering Thermodynamics, Heat and Mass Transfer.

Local Subjects:

Computational Intelligence.

Machine Learning.

Engineering Thermodynamics, Heat and Mass Transfer.

Physical Description:

1 online resource (108 pages)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

Summary:

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. .

Contents:

Introduction

Fundamental Concepts of Machine Learning

Neural Networks

Machine Learning in Physics and Engineering

Physics-informed Neural Networks

Deep Energy Method.

ISBN:

3-030-76587-3

OCLC:

1263873751