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English
Oxford University Press
01 August 2006
This book discusses the computational approach in modern statistical physics in a clear yet accessible way, and works out its intimate relations with other approaches in theoretical physics. Individual chapters focus on subjects as diverse as the hard sphere liquid, classical spin models, single quantum particles and Bose-Einstein condensation. They contain in-depth discussions of algorithms ranging from basic enumeration methods to modern Monte Carlo techniques. The emphasis is on orientation. Discussions of implementation details are kept to a minimum.

The book heavily relies on illustrations, tables and concise printed algorithms to convey key information: all the material remains easily accessible.

The book is fully self-contained: graphs and tables can be readily reproduced by programming at most a few dozen lines of computer code. Most sections lead from an elementary discussion to the rich and difficult problems of contemporary computational and statistical physics, and will be of interest to a wide range of students, teachers and researchers in physics and the neighboring sciences. A companion website allows to incorporate the layout material (illustrations, tables, schematic programs) into the reader's own presentations.

By:  
Imprint:   Oxford University Press
Country of Publication:   United Kingdom
Volume:   No. 13
Dimensions:   Height: 246mm,  Width: 189mm,  Spine: 18mm
Weight:   839g
ISBN:   9780198515364
ISBN 10:   0198515367
Series:   Oxford Master Series in Physics
Pages:   360
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

Reviews for Statistical Mechanics: Algorithms and Computations

`... a very timely topic. The role of computation in statistical mechanics is increasingly important, ... the inclusion of non-equilibrium problems, quantum systems, and the Bose gas brings this volume bang up to date.' John Cardy, University of Oxford `... timely and fascinating, ... with a very attractive focus on the structure and efficiency of algorithms.' Paul Goldbart, University of Illinois at Urbana-Champaign


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