Deep learning in computational mechanics : an introductory course / Stefan Kollmannsberger ... [et al.].
- 其他作者：
- 其他題名：
- Studies in computational intelligence ;
- 出版： Cham, Switzerland : Springer c2021.
- 叢書名： Studies in computational intelligence ;v. 977
- 主題： Machine learning. , Neural networks (Computer science)
- ISBN： 9783030765866 (hbk.) :: NT$2564 、 3030765865 (hbk.)
- 書目註：Includes bibliographical references and index.
-
讀者標籤：
- 系統號： 005174476 | 機讀編目格式
館藏資訊
摘要註
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.
內容註
Introduction -- Fundamental Concepts of Machine Learning -- Neural Networks -- Machine Learning in Physics and Engineering -- Physics-informed Neural Networks -- Deep Energy Method.