Computational Physics With Python
by Eric Ayars
Publisher: California State University, Chico 2013
Number of pages: 194
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; Least-Squares Fitting.
Download or read it online for free here:
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 K. P. N. Murthy - arXiv
An introduction to the basics of Monte Carlo is given. The topics covered include sample space, events, probabilities, random variables, mean, variance, covariance, characteristic function, chebyshev inequality, law of large numbers, etc.
by Matthias Bolten - John von Neumann Institute for Computing
This work is focused on the application of multigrid methods to particle simulation methods. Particle simulation is important for a broad range of scientific fields, like biophysics, astrophysics or plasma physics, to name a few.
by Richard Fitzpatrick
The purpose of the text is to demonstrate how computers can help deepen our understanding of physics and increase the range of calculations which we can perform. These lecture notes are writen for an undergraduate course on computational physics.