Deep learning and physics / Akinori Tanaka, Akio Tomiya, Koji Hashimoto.
Tanaka, Akinori, author.; Tomiya, Akio, author.; Hashimoto, Kōji (Physicist), author.
2021
QC52
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Deep learning and physics / Akinori Tanaka, Akio Tomiya, Koji Hashimoto.
Author Tanaka, Akinori, author.
ISBN 9813361085 (electronic book)
9789813361096 (print)
9813361093
9789813361102 (print)
9813361107
9789813361089 (electronic bk.)
9813361077
9789813361072
DOI https://doi.org/10.1007/978-981-33-6108-9
Published Singapore : Springer, [2021]
Language English
Description 1 online resource (XIII, 207 pages 46 illustrations, 29 illustrations in color.) : online resource
Item Number 10.1007/978-981-33-6108-9 doi
Call Number QC52
Dewey Decimal Classification 530.0285
Summary What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 17, 2021).
Added Author Tomiya, Akio, author.
Hashimoto, Kōji (Physicist), author.
Series Mathematical physics studies.
Available in Other Form Print version: 9789813361072
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Forewords: Machine learning and physics
Part I Physical view of deep learning. Introduction to machine learning ; Basics of neural networks ; Advanced neural networks ; Sampling ; Unsupervised deep learning
Part II Applications to physics. Inverse problems in physics ; Detection of phase transition by machines ; Dynamical systems and neural networks ; Spinglass and neural networks ; Quantum manybody systems, tensor networks and neural networks ; Application to superstring theory
Epilogue.

Browse Subjects

Show more subjects...

Statistics

from
to
Export