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Book Title: An Introduction to Computational Physics, 2nd Edition
Author: Tao Pang
Publisher: Cambridge University Press
Publication Place: Cambridge, United Kingdom
Publication Date: February 2006
ISBN: 0-521-82569-5 (hardback)
List Price: $70.00
Other Info: 402 Pages; 246 x 189 mm; 37 Line Diagrams; 3 Tables; 169 Exercises; Bibliography; and Index
An Introduction to Computational Physics, 2nd Edition:
Numerical simulation has become an integrated part of science and technology. Now in its second edition, this comprehensive textbook provides an introduction to the basic methods of computational physics, as well as an overview of recent progress in scientific computing. The author presents many step-by-step examples from modern physics and related areas in which computational physics has made significant progress in the last decade, often with program listings in Java.

The first half of the book deals with basic computational tools and routines, covering approximation of a function, numerical calculus and optimization of a function, differential equations, schemes to solve matrix and linear equation problems, and spectral analysis and Gaussian quadratures. Important concepts are illustrated from many revealing examples at each stage. In the second half of the book, the author also discusses more advanced topics, such as partial differential equations, molecular dynamics simulation, modeling continuous systems, Monte Carlo methods, genetic algorithm and programming, and numerical renormalization.

The new edition has been thoroughly revised and includes many more examples and exercises. It can be used as a textbook for either undergraduate or first-year graduate courses on computational physics or scientific computation. It will also be a useful reference for anyone involved in computational research.

1. Introduction; 2. Approximation of a function; 3. Numerical calculus; 4. Ordinary differential equations; 5. Numerical methods for matrices; 6. Spectral analysis; 7. Partial differential equations; 8. Molecular dynamics simulations; 9. Modeling continuous systems; 10. Monte Carlo simulations; 11. Genetic algorithm and programming; 12. Numerical renormalization; References; Index.

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