Buchcover Computational Statistical Physics | EAN 9783662048047 | ISBN 3-662-04804-3 | ISBN 978-3-662-04804-7

From the reviews of the first edition:

„This book contains 17 chapters devoted to the use of statistical mechanical methods in areas not belonging to physics and also a few which do belong to physics. Each chapter is a nicely written tutorial presentation of the problem and of the numerical techniques used to solve it. … Each of these chapters can be used as a concise introduction … .“ (M. Baus, Physicalia, Issue 6, 2002)

„This book is a different style of computational physics text, rather more like the conference proceedings than the usual undergraduate text. … The result then is quite a pleasing survey of current topics in computational statistical physics. … For the lecturer this is a very attractive resource for project length problems in a computational physics course for higher undergraduate or early graduate level students. For graduate students it is a good survey of modern statistical physics problems that lend themselves to numerical treatment.“ (G. P. Morriss, The Physicist, Vol. 39 (3), 2002)

Computational Statistical Physics

From Billiards to Monte Carlo

herausgegeben von K.-H. Hoffmann und Michael Schreiber

Mitwirkende
Herausgegeben von	K.-H. Hoffmann
Herausgegeben von	Michael Schreiber

In recent years statistical physics has made significant progress as a result of advances in numerical techniques. While good textbooks exist on the general aspects of statistical physics, the numerical methods and the new developments based on large-scale computing are not usually adequately presented. In this book 16 experts describe the application of methods of statistical physics to various areas in physics such as disordered materials, quasicrystals, semiconductors, and also to other areas beyond physics, such as financial markets, game theory, evolution, and traffic planning, in which statistical physics has recently become significant. In this way the universality of the underlying concepts and methods such as fractals, random matrix theory, time series, neural networks, evolutionary algorithms, becomes clear. The topics are covered by introductory, tutorial presentations.