by Angus MacKinnon
Publisher: Imperial College London 2002
Number of pages: 48
This course aims to give the student a thorough grounding in the main computational techniques used in modern physics. This is not a text in computing science, nor in programming. It focuses specifically on methods for solving physics problems.
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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.
by Werner Krauth - CNRS-Laboratoire de Physique Statistique
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, etc.
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 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.