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001434422 019__ $$a1238192168$$a1244118914$$a1253408837$$a1264875749$$a1268409979
001434422 020__ $$a9813361085$$q(electronic book)
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001434422 0247_ $$a10.1007/978-981-33-6108-9$$2doi
001434422 035__ $$aSP(OCoLC)1239991463
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001434422 08204 $$a530.0285$$223
001434422 1001_ $$aTanaka, Akinori,$$eauthor.
001434422 24510 $$aDeep learning and physics /$$cAkinori Tanaka, Akio Tomiya, Koji Hashimoto.
001434422 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001434422 300__ $$a1 online resource (XIII, 207 pages 46 illustrations, 29 illustrations in color.) :$$bonline resource
001434422 336__ $$atext$$btxt$$2rdacontent
001434422 337__ $$acomputer$$bc$$2rdamedia
001434422 338__ $$aonline resource$$bcr$$2rdacarrier
001434422 347__ $$atext file
001434422 347__ $$bPDF
001434422 4901_ $$aMathematical physics studies
001434422 504__ $$aIncludes bibliographical references and index.
001434422 5050_ $$aForewords: Machine learning and physics -- Part I Physical view of deep learning. Introduction to machine learning ; Basics of neural networks ; Advanced neural networks ; Sampling ; Unsupervised deep learning -- Part II Applications to physics. Inverse problems in physics ; Detection of phase transition by machines ; Dynamical systems and neural networks ; Spinglass and neural networks ; Quantum manybody systems, tensor networks and neural networks ; Application to superstring theory -- Epilogue.
001434422 506__ $$aAccess limited to authorized users.
001434422 520__ $$aWhat is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.
001434422 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 17, 2021).
001434422 650_0 $$aPhysics$$xData processing.
001434422 650_0 $$aMachine learning.
001434422 650_6 $$aPhysique$$xInformatique.
001434422 650_6 $$aApprentissage automatique.
001434422 655_0 $$aElectronic books.
001434422 7001_ $$aTomiya, Akio,$$eauthor.
001434422 7001_ $$aHashimoto, Kōji$$c(Physicist),$$eauthor.
001434422 77608 $$iPrint version:$$z9789813361072
001434422 830_0 $$aMathematical physics studies.
001434422 852__ $$bebk
001434422 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-6108-9$$zOnline Access$$91397441.1
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