Deep learning and physics / Akinori Tanaka, Akio Tomiya, Koji Hashimoto.

Author/creator Tanaka, Akinori author.
Other author Tomiya, Akio, author.
Other author Hashimoto, Kōji (Physicist), author.
Format Electronic
PublicationSingapore : Springer, [2021]
Description1 online resource (XIII, 207 p. 46 illus., 29 illus. in color. :) online resource.
Supplemental ContentEbook Central
Subjects
Physics--Data processing.
Machine learning.
Electronic books.

SeriesMathematical physics studies
Mathematical physics studies. ^A271183
Contents 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.
Abstract 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 noteIncludes bibliographical references and index.
Source of descriptionOnline resource; title from PDF title page (SpringerLink, viewed March 17, 2021).
Issued in other formPrint version: 9789813361072
Genre/formElectronic books.
ISBN9813361085 electronic book
ISBN9789813361096 (print)
ISBN9813361093
ISBN9789813361102 (print)
ISBN9813361107
ISBN9789813361089 (electronic bk.)
Standard identifier# 10.1007/978-981-33-6108-9
Stock numberSpringer