Monte Carlo Methods in Statistical Physics


M. E. J. Newman and G. T. Barkema

This book provides a clear, self-contained and authoritative introduction to Monte Carlo simulations in classical statistical physics. It is written both for students who are just beginning work in the field and for more experienced researchers who wish to expand their knowledge of Monte Carlo techniques.

The material covered includes methods for both equilibrium and out-of-equilibrium systems. Common algorithms like the Metropolis and heat-bath algorithms are described in detail, as well as more sophisticated ones such as continuous time Monte Carlo, cluster algorithms, multigrid methods, entropic sampling and simulated tempering. Many example applications are discussed ranging from simple models such as Ising models and spin glasses to real-world problems like molecular beam epitaxy and polymer electrophoresis. Data analysis techniques are also explained, starting with straightforward measurement and error-estimation, and progressing to topics such as the single and multiple histogram methods and finite size scaling. The last few chapters of the book are devoted to implementation issues, including discussions of such topics as lattice representations, efficient implementation of data structures, multispin coding, parallelization of Monte Carlo algorithms, and random number generation. Exercises for the reader are included at the end of each chapter and a number of example programs demonstrating the efficient implementation of Monte Carlo techniques are given at the end of the book.

About the authors:

Mark Newman received his Ph.D. in theoretical physics from the University of Oxford. Since then, he has held positions in the Physics Department at Cornell University and at the Santa Fe Institute in Santa Fe, New Mexico. Currently he is an Assistant Professor of Physics and Complex Systems at the University of Michigan. His research interests are in the development of Monte Carlo algorithms and novel applications of the Monte Carlo method, particularly in the study of glassy systems and complex systems.

Gerard Barkema received his Ph.D. in computational physics from Utrecht University. Since then, he has held positions at Cornell University, the University of Oxford, the Institute for Advanced Study in Princeton, and the Hoechstleistungsrechenzentrum in Juelich. Currently he is a member of faculty in the physics department at Utrecht University. His research interests are in the development of Monte Carlo algorithms and their applications, particularly in polymer physics and glassy systems.

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Monte Carlo Methods in Statistical Physics is published by
Oxford University Press.

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