A self-contained graduate-level introduction to the statistical mechanics of disordered systems. Series Editor(s): Gill, R.; Ripley, Brian D.; Ross, S.; Silverman, B.W. (St Peter's College, Oxford); Stein, M. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 328 pages, 30 figures. BIC Classification: PB. Category: (P) Professional & Vocational. Dimension: 254 x 178 x 19. Weight in Grams: 806. . 2006. Illustrated. hardcover. . . . .

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Bibliographic Details

Title: Statistical Mechanics of Disordered Systems: A Mathematical Perspective: 18 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 18)
Publisher: Cambridge University Press
Publication Date: 2006
Language: English
ISBN 10: 0521849918
ISBN 13: 9780521849913
Binding: Hardcover
Condition: New

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Synopsis

This self-contained book is a graduate-level introduction for mathematicians and for physicists interested in the mathematical foundations of the field, and can be used as a textbook for a two-semester course on mathematical statistical mechanics. It assumes only basic knowledge of classical physics and, on the mathematics side, a good working knowledge of graduate-level probability theory. The book starts with a concise introduction to statistical mechanics, proceeds to disordered lattice spin systems, and concludes with a presentation of the latest developments in the mathematical understanding of mean-field spin glass models. In particular, progress towards a rigorous understanding of the replica symmetry-breaking solutions of the Sherrington-Kirkpatrick spin glass models, due to Guerra, Aizenman-Sims-Starr and Talagrand, is reviewed in some detail.

Book Description

A self-contained graduate-level introduction to the statistical mechanics of disordered systems. In three parts, the book treats basic statistical mechanics; disordered lattice spin systems; and latest developments in the mathematical understanding of mean-field spin glass models. It assumes basic knowledge of classical physics and working knowledge of graduate-level probability theory.

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