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Deep Learning and Physics

  • Book
  • © 2021


  • 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

Part of the book series: Mathematical Physics Studies (MPST)

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Table of contents (13 chapters)

  1. Physical View of Deep Learning

  2. Applications to Physics


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 providesprogress 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.


“The book has the feel of a graduate thesis. It could be quite useful to a researcher investigating the relationship between ANNs and dynamical physical systems.” (Anoop Malaviya, Computing Reviews, February 16, 2023)

Authors and Affiliations

  • iTHEMS, RIKEN, Wako, Japan

    Akinori Tanaka

  • Radiation Lab, RIKEN, Wako, Japan

    Akio Tomiya

  • Department of Physics, Osaka University, Toyonaka, Japan

    Koji Hashimoto

About the authors

Akinori Tanaka, Akio Tomiya, Koji Hashimoto

Bibliographic Information

  • Book Title: Deep Learning and Physics

  • Authors: Akinori Tanaka, Akio Tomiya, Koji Hashimoto

  • Series Title: Mathematical Physics Studies

  • DOI:

  • Publisher: Springer Singapore

  • eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021

  • Hardcover ISBN: 978-981-33-6107-2Published: 21 February 2021

  • Softcover ISBN: 978-981-33-6110-2Published: 22 February 2022

  • eBook ISBN: 978-981-33-6108-9Published: 20 February 2021

  • Series ISSN: 0921-3767

  • Series E-ISSN: 2352-3905

  • Edition Number: 1

  • Number of Pages: XIII, 207

  • Number of Illustrations: 17 b/w illustrations, 29 illustrations in colour

  • Topics: Theoretical, Mathematical and Computational Physics, Mathematical Physics, Machine Learning

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