Synopses & Reviews
This advanced textbook provides an introduction to the basic methods of computational physics, as well as an overview of recent progress in several areas of scientific computing. The author presents many step-by-step examples of practical numerical methods, often with the relevant program listing. The first half of the book deals with basic computational tools and routines, covering differential equations, spectral analysis and matrix operations. Important concepts are illustrated by relevant examples at each stage. The author also discusses more advanced topics, such as Monte Carlo simulations, lattice gas methods, molecular dynamics and symbolic computing. The book includes many exercises, and it can be used as a textbook for senior undergraduate or first-year graduate courses on scientific computation. It will also be a useful reference for anyone involved in computational physics or related disciplines.
Review
"...an excellent resource for beginning researchers in computational physics." Choice"...touches on everything from linear interpolation to quantum lattice renormalization....a bridge between introductory material and detailed discussions of more advanced applications." Physics Today"On the whole, this book represents a valuable addition as a complementary computational physics textbook at upper advanced undergraduate and graduate level. It is well written, with accent on the most recent developments in computing techniques. The book may be very helpful as a reference source for scientists with recurrent use of computational techniques." Materials Research Bulletin
Synopsis
Textbook introducing basic methods of computational physics and giving overview of several advanced topics; for advanced undergraduate or beginning graduate students.
Description
Includes bibliographical references (p. 357-365) and index.
Table of Contents
Preface; Acknowledgement; 1. Introduction; 2. Basic numerical methods; 3. Ordinary differential equations; 4. Numerical methods for matrices; 5. Spectral analysis and Gaussian quadrature; 6. Partial differential equations; 7. Molecular dynamics simulations; 8. Modeling continuous systems; 9. Monte Carlo simulations; 10. Numerical renormalization; 11. Symbolic computing; 12. High performance computing.