Deep learning in computational mechanics : an introductory course
Author(s)
Bibliographic Information
Deep learning in computational mechanics : an introductory course
（Studies in computational intelligence, v. 977）
Springer, c2021
Available at 5 libraries
Note
Other author: Davide D'Angella, Moritz Jokeit, Leon Herrmann
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
Introduction.- Fundamental Concepts of Machine Learning.- Neural Networks.- Machine Learning in Physics and Engineering.- Physics-informed Neural Networks.- Deep Energy Method
by "Nielsen BookData"