Computational Statistical Physics

In this book, 16 experts describe the application of methods of statistical physics to various areas in physics: 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.

1 Game Theory and Statistical Mechanics.- 2 Chaotic Billiards.- 3 Combinatorial Optimization and High Dimensional Billiards.- 4 The Statistical Physics of Energy Landscapes: From Spin Glasses to Optimization.- 5 Optimization of Production Lines by Methods from Statistical Physics.- 6 Predicting and Generating Time Series by Neural Networks: An Investigation Using Statistical Physics.- 7 Statistical Physics of Cellular Automata Models for Traffic Flow.- 8 Self-Organized Criticality in Forest-Fire Models.- 9 Nonlinear Dynamics of Active Brownian Particles.- 10 Financial Time Series and Statistical Mechanics.- 11 'Go with the Winners' Simulations.- 12 Aperiodicity and Disorder - Do They Play a Role?.- 13 Quantum Phase Transitions.- 14 Introduction to Energy Level Statistics.- 15 Randomness in Optical Spectra of Semiconductor Nanostructures.- 16 Characterization of the Metal-Insulator Transition in the Anderson Model of Localization.- 17 Percolation, Renormalization and Quantum Hall Transition.

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)