An Introduction to Monte Carlo Simulations in Statistical Physics
by K. P. N. Murthy
Publisher: arXiv 2003
Number of pages: 92
A brief introduction to the technique of Monte Carlo simulations in statistical physics is presented. The topics covered include statistical ensembles random and pseudo random numbers, random sampling techniques, importance sampling, Markov chain, Metropolis algorithm, continuous phase transition, statistical errors from correlated and uncorrelated data, finite size scaling, n-fold way, critical slowing down, blocking technique,percolation, cluster algorithms, etc.
Home page url
Download or read it online for free here:
by Michael Cross - Caltech
The author discusses using statistical mechanics to understand real systems, rather than ideal systems that can be solved exactly. In addition dynamics and fluctuations are considered. These notes are an attempt to summarize the main points.
by Eric L. Michelsen - UCSD
This work is aimed at graduate and advanced undergraduate physics students. It contains a better entropy discussion, the Carnot conspiracy, Boltzmann distribution, entropy, free energy, meet Mr. Mole, chemical potential, and much more...
by Hugo Touchette - arXiv
The theory of large deviations deals with the probabilities of rare events that are exponentially small as a function of some parameter, e.g., the number of random components of a system or the time over which a stochastic system is observed.
by Olivier Sarbach, Thomas Zannias - arXiv
A brief introduction to the relativistic kinetic theory of gases with emphasis on the underlying geometric and Hamiltonian structure of the theory. We start with a discussion on the tangent bundle of a Lorentzian manifold of arbitrary dimension...