Monte Carlo: Basics
by K. P. N. Murthy
Publisher: arXiv 2001
Number of pages: 76
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, central limit theorem (stable distribution, Levy distribution), random numbers (generation and testing), random sampling techniques (inversion, rejection, sampling from a Gaussian, Metropolis sampling), analogue Monte Carlo and Importance sampling (exponential biasing, spanier technique).
Home page url
Download or read it online for free here:
by Angus MacKinnon - Imperial College London
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.
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.
by Johannes Grotendorst, Stefan Bluegel, Dominik Marx - NIC
This volume focuses on the application of electronic structure calculations and dynamical simulation techniques covering aspects of solid state physics, surface and nanoscience, chemical reactions and dynamics, magnetism and electron transport, etc.