Pub. Date:
Oxford University Press
Monte Carlo Methods in Statistical Physics / Edition 1

Monte Carlo Methods in Statistical Physics / Edition 1

by M. E. J. Newman, G. T. Barkema


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This book provides an introduction to Monte Carlo simulations in classical statistical physics and is aimed both at students beginning work in the field and at more experienced researchers who wish to learn more about Monte Carlo methods. It includes methods for both equilibrium and out of equilibrium systems, and discusses in detail such common algorithms as the Metropolis and heat-bath algorithms, as well as more sophisticated ones such as continuous time Monte Carlo, cluster algorithms, multigrid methods, entropic sampling and simulated tempering. Data analysis techniques are also explained starting with straightforward measurement and error-estimation techniques 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 lattice representations, efficient implementation of data structures, multispin coding, parallelization of Monte Carlo algorithms, and random number generation. The book also includes example programs which show how to apply these techniques to a variety of well-known models.

Product Details

ISBN-13: 9780198517979
Publisher: Oxford University Press
Publication date: 04/15/1999
Edition description: New Edition
Pages: 496
Product dimensions: 9.20(w) x 6.10(h) x 1.00(d)

Table of Contents

I. Equilibrium Monte Carlo calculations
1. Introduction
2. The principles of equilibrium thermal Monte Carlo simulations
3. The Ising model and the Metropolis algorithm
4. Other algorithms for the Ising model
5. The conserved-order-parameter Ising model
6. Disordered spin models
7. Ice models
8. Analysing Monte Carlo data
II. Out-of-equilibrium calculations
9. Principles of out-of-equilibrium Monte Carlo simulation
10. Non-equilibrium simulations of the Ising model
11. Monte Carlo simulations in surface science
12. The repton model
III. Implementation
13. Lattices and data structures
14. Monte Carlo simulations on parallel computers
15. Multispin coding
16. Random numbers

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