Mathematical Physics Studies

Deep Learning and Physics

Authors: Tanaka, Akinori, Tomiya, Akio, Hashimoto, Koji

Free Preview
  • Is the first machine learning textbook written by physicists so that physicists and undergraduates can learn easily
  • Presents applications to physics problems written so that readers can soon imagine how machine learning is to be used 
  • Offers the starting point for researchers in the rapidly growing field of physics and machine learning 
see more benefits

Buy this book

eBook $109.00
price for USA in USD
  • Due: April 21, 2021
  • ISBN 978-981-336-108-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
Hardcover $139.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: February 20, 2021
  • ISBN 978-981-336-107-2
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
About this book

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.

About the authors

Akinori Tanaka, Akio Tomiya, Koji Hashimoto

Table of contents (13 chapters)

Table of contents (13 chapters)

Buy this book

eBook $109.00
price for USA in USD
  • Due: April 21, 2021
  • ISBN 978-981-336-108-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
Hardcover $139.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: February 20, 2021
  • ISBN 978-981-336-107-2
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Deep Learning and Physics
Authors
Series Title
Mathematical Physics Studies
Copyright
2021
Publisher
Springer Singapore
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
eBook ISBN
978-981-336-108-9
DOI
10.1007/978-981-33-6108-9
Hardcover ISBN
978-981-336-107-2
Series ISSN
0921-3767
Edition Number
1
Number of Pages
XIII, 207
Number of Illustrations
17 b/w illustrations, 29 illustrations in colour
Topics