Monte Carlo methods in statistical physics (Topics in current physics)

Science & Math

Kurt Binder

Physics

Springer, Verlag

<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)

Dieter W. Heermann

Mathematics

A Guide to Monte Carlo Simulations in Statistical Physics

David P. Landau

Cambridge University Press

Monte Carlo Methods in Statistical Physics (Topics in Current Physics)

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

The "Monte Carlo method" is a method of computer simulation of a system with many degrees of freedom, and thus it has widespread applications in science. It takes its name from the use of random numbers to simulate statistical fluctuations in order to numerically gen- erate probability distributions (which cannot otherwise be known explicitly, since the systems considered are so complex). The Monte Carlo method then yields numerically exact information on "model systems". Such simulations serve two purposes: one can check the extent to which a model system approximates a real system; or one may check the validity of approximations made in analytical theories. This book summarizes recent progress obtained in the implementation of this method and with the general analysis of results, and gives concise reviews of recent applications. These applications include simulations of growth processes far from equilibrium, interfacial phenomena, quantum and classical fluids, polymers, quantum problems on lattices, and random systems.

The Monte Carlo Method in Condensed Matter Physics (Topics in Applied Physics)

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 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) 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 introduction to the theory and practice of Monte Carlo simulations.

Monte Carlo methods in statistical physics: An introduction (Springer series in solid state sciences)

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)

Computers & Technology

Computer Science

<p>The physics of glassy materials and disordered solids presents students with an area of study much more challenging than the physics of crystalline solids. Written by two recognized experts in the field, this highly readable book tackles the subject with the student firmly in mind, beginning with a pedagogical introduction to important concepts such as percolation, fractals, spin glasses, and glasses. Making use of these concepts, the authors show that such systems share many common aspects that can be described within the framework of statistical mechanics. The book is also an essential standard text for researchers on amorphous materials, equally accessible for theorists and experimentalists.</p>

GLASSY MATERIALS AND DISORDERED SOLIDS: AN INTRODUCTION TO THEIR STATISTICAL MECHANICS

Walter Kob

World Scientific Publishing Company

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Books by Kurt Binder