Introduction To Monte Carlo Algorithms
by Werner Krauth
Publisher: CNRS-Laboratoire de Physique Statistique 1998
Number of pages: 43
In these lectures, the author discusses the fundamental principles of thermodynamic and dynamic Monte Carlo methods in a simple light-weight fashion. The keywords are Markov chains, Sampling, Detailed Balance, A Priori Probabilities, Rejections, Ergodicity, "Faster than the clock algorithms".
Home page url
Download or read it online for free here:
by Mark Newman - University of Michigan
The Python programming language is an excellent choice for learning, teaching, or doing computational physics. This page contains a selection of resources the author developed for teachers and students interested in computational physics and Python.
by Michael P. Brenner - Harvard University
This is an introduction to mathematical methods for solving hard mathematics problems that arise in the sciences -- physical, biological and social. Our aim therefore is to teach how computer simulations and analytical calculations can be combined.
by Allen B. Downey - Green Tea Press
An introduction to physical modeling using a computational approach. Taking a computational approach makes it possible to work with more realistic models than what you typically see in a first-year physics class, such as friction and drag.
by Johan Hoffman, Claes Johnson
Computational foundation of thermodynamics based on deterministic finite precision computation without resort to statistics. A new 2nd Law without the concept of entropy is proved to be a consequence of the 1st Law and finite precision computation.