Computer-aided Statistical Physics PDF Download
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Languages : en
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Languages : en
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Author: Library of Congress
Publisher:
ISBN:
Category : Subject headings, Library of Congress
Languages : en
Pages : 1624
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Author: Library of Congress. Office for Subject Cataloging Policy
Publisher:
ISBN:
Category : Subject headings, Library of Congress
Languages : en
Pages : 1692
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Author: Library of Congress. Cataloging Policy and Support Office
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ISBN:
Category : Subject headings, Library of Congress
Languages : en
Pages : 1512
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Author: Bernard P. Veldkamp
Publisher: Springer
ISBN: 3030184803
Category : Education
Languages : en
Pages : 394
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Book Description
This open access book presents a large number of innovations in the world of operational testing. It brings together different but related areas and provides insight in their possibilities, their advantages and drawbacks. The book not only addresses improvements in the quality of educational measurement, innovations in (inter)national large scale assessments, but also several advances in psychometrics and improvements in computerized adaptive testing, and it also offers examples on the impact of new technology in assessment. Due to its nature, the book will appeal to a broad audience within the educational measurement community. It contributes to both theoretical knowledge and also pays attention to practical implementation of innovations in testing technology.
Author: Robert P H Gasser
Publisher: World Scientific Publishing Company
ISBN: 9813103132
Category : Science
Languages : en
Pages : 190
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Book Description
Statistical thermodynamics plays a vital linking role between quantum theory and chemical thermodynamics, yet students often find the subject unpalatable.In this updated version of a popular text, the authors overcome this by emphasising the concepts involved, in particular demystifying the partition function. They do not get bogged down in the mathematical niceties that are essential for a profound study of the subject but which can confuse the beginner. Strong emphasis is placed on the physical basis of statistical thermodynamics and the relations with experiment. After a clear exposition of the distribution laws, partition functions, heat capacities, chemical equilibria and kinetics, the subject is further illuminated by a discussion of low-temperature phenomena and spectroscopy. The coverage is brought right up to date with a chapter on computer simulation and a final section which ranges beyond the narrow limits usually associated with student texts to emphasise the common dependence of macroscopic behaviour on the properties of constituent atoms and molecules.Since first published in 1974 as ‘Entropy and Energy Levels’, the book has been very popular with students. This revised and updated version will no doubt serve the same needs.
Author: Gerard G. Emch
Publisher: Springer Science & Business Media
ISBN: 3662048868
Category : Science
Languages : en
Pages : 707
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Book Description
This book is devoted to a thorough analysis of the role that models play in the practise of physical theory. The authors, a mathematical physicist and a philosopher of science, appeal to the logicians’ notion of model theory as well as to the concepts of physicists.
Author: Akinori Tanaka
Publisher: Springer Nature
ISBN: 9813361085
Category : Science
Languages : en
Pages : 211
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Book Description
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.
Author:
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ISBN:
Category : Engineering
Languages : en
Pages : 836
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Author: Fa Yueh Wu
Publisher: World Scientific
ISBN: 9812813888
Category : Science
Languages : en
Pages : 661
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Book Description
Organized into topics ranging from lattice models in condensed matter physics to graph theory in mathematics. This title presents an overview of each of the topics and a look at how crucial developments emerged.
