Mathematical Physics Studies

Deep Learning and Physics

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

Vorschau
  • 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
Weitere Vorteile

Dieses Buch kaufen

eBook 93,08 €
Preis für Deutschland (Brutto)
  • ISBN 978-981-336-108-9
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: EPUB, PDF
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 117,69 €
Preis für Deutschland (Brutto)
Über dieses Buch

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.

Über die Autor*innen

Akinori Tanaka, Akio Tomiya, Koji Hashimoto

Inhaltsverzeichnis (13 Kapitel)

Inhaltsverzeichnis (13 Kapitel)

Dieses Buch kaufen

eBook 93,08 €
Preis für Deutschland (Brutto)
  • ISBN 978-981-336-108-9
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: EPUB, PDF
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 117,69 €
Preis für Deutschland (Brutto)
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Bibliografische Information

Bibliographic Information
Buchtitel
Deep Learning and Physics
Autoren
Titel der Buchreihe
Mathematical Physics Studies
Copyright
2021
Verlag
Springer Singapore
Copyright Inhaber
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
Buchreihen ISSN
0921-3767
Auflage
1
Seitenzahl
XIII, 207
Anzahl der Bilder
17 schwarz-weiß Abbildungen, 29 Abbildungen in Farbe
Themen