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 Volker Springel - arXiv
These are lecture notes about high performance computing and numerical modelling in 43rd Saas Fee Advanced Course winter school, specifically covering the basics of numerically treating gravity and hydrodynamics in the context of galaxy evolution.
by Jeffrey R. Chasnov - Harvey Mudd College
This course consists of both numerical methods and computational physics. MATLAB is used to solve various computational math problems. The course is primarily for Math majors and supposes no previous knowledge of numerical analysis or methods.
by T. H. Pulliam - NASA
Implicit finite difference schemes for solving two dimensional and three dimensional Euler and Navier-Stokes equations will be addressed. The methods are demonstrated in fully vectorized codes for a CRAY type architecture.
by Eric Ayars - California State University, Chico
Contents: Useful Introductory Python; Python Basics; Basic Numerical Tools; Numpy, Scipy, and MatPlotLib; Ordinary Differential Equations; Chaos; Monte Carlo Techniques; Stochastic Methods; Partial Differential Equations; Linux; Visual Python; etc.