A Guide to Monte Carlo Simulations in Statistical Physics
This new and updated edition deals with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics, statistical mechanics, and related fields. After briefly recalling essential background in statistical mechanics and probability theory, it gives a succinct overview of simple sampling methods. The concepts behind the simulation algorithms are explained comprehensively, as are the techniques for efficient evaluation of system configurations generated by simulation. It contains many applications, examples, and exercises to help the reader and provides many new references to more specialized literature. This edition includes a brief overview of other methods of computer simulation and an outlook for the use of Monte Carlo simulations in disciplines beyond physics. This is an excellent guide for graduate students and researchers who use computer simulations in their research. It can be used as a textbook for graduate courses on computer simulations in physics and related disciplines.
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Some necessary background
Simple sampling Monte Carlo methods
Importance sampling Monte Carlo methods
More on importance sampling Monte Carlo methods
Quantum Monte Carlo methods
Monte Carlo renormalization group methods
Other editions - View all
algorithm applied approach average becomes behavior Binder bonds boundary conditions calculated Chapter chosen cluster completely configuration consider constant continue correlation coupling course critical density dependence described determine dimensions direction discussed distribution dynamics effective energy ensemble equation equilibrium error estimate et al example exponent field finite flipping fluctuations fluid function given growth Hamiltonian important initial integration interactions interest Ising model Landau lattice length limit magnetization mean method Monte Carlo methods Monte Carlo simulations move nearest neighbor needed Note obtained occur order parameter particles periodic phase phase transition Phys physics polymer possible potential probability problem produce properties quantities random number range relaxation sampling scaling shown simple single space spin square statistical step structure surface techniques temperature theory tion transition variables write