Computer Simulation Methods in Theoretical Physics

Computers & Technology

Dieter W. Heermann

Computer Science

Springer-Verlag New York, Inc.

<p>The Monte Carlo method is a computer simulation method which uses random numbers to simulate statistical fluctuations. The method is used to model complex systems with many degrees of freedom. Probability distributions for these systems are generated numerically and the method then yields numerically exact information on the models. Such simulations may be used to see how well a model system approximates a real one or to see how valid the assumptions are in an analyical theory. A short and systematic theoretical introduction to the method forms the first part of this book. The second part is a practical guide with plenty of examples and exercises for the student. Problems treated by simple sampling (random and self-avoiding walks, percolation clusters, etc.) are included, along with such topics as finite-size effects and guidelines for the analysis of Monte Carlo simulations. The two parts together provide an excellent introduction to the theory and practice of Monte Carlo simulations.</p>

Monte Carlo Simulation in Statistical Physics: An Introduction (Springer Series in Solid-state Sciences)

Science & Math

Kurt Binder

Mathematics

Springer, Verlag

Springer

The Monte Carlo method is a computer simulation method which uses random numbers to simulate statistical fluctuations. The method is used to model complex systems with many degrees of freedom. Probability distributions for these systems are generated numerically and the method then yields numerically exact information on the models. Such simulations may be used to see how well a model system approximates a real one or to see how valid the assumptions are in an analytical theory. A short and systematic theoretical introduction to the method forms the first part of this book. The second part is a practical guide with plenty of examples and exercises for the student. Problems treated by simple sampling (random and self-avoiding walks, percolation clusters, etc.) and by importance sampling (Ising models etc.) are included, along with such topics as finite-size effects and guidelines for the analysis of Monte Carlo simulations. The two parts together provide an excellent introduction to the theory and practice of Monte Carlo simulations.

Monte Carlo Simulation in Statistical Physics

Monte Carlo Simulation in Statistical Physics deals with the computer simulation of many-body systems in condensed-matter physics and related fields of physics, chemistry and beyond, to traffic flows, stock market fluctuations, etc) Using random numbers generated by a computer, probability distributions are calculated, allowing the estimation of the thermodynamic properties of various systems. This book describes the theoretical background to several variants of these Monte Carlo methods and gives a systematic presentation from which newcomers can learn to perform such simulations and to analyze their results. This fourth edition has been updated and a new chapter on Monte Carlo simulation of quantum-mechanical problems has been added. To help students in their work a special web server has been installed to host programs and discussion groups (http:wwwcp.tphys.uni-heidelberg.de) Prof. Binder was the winner of the Berni J. Alder CECAM Award for Computational Physics 2001.

Monte Carlo Simulation in Statistical Physics: An Introduction (Springer Series in Solid-State Sciences)

Simulation butsurigaku (Japanese Edition)

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