This new edition is a concise introduction to
the basic methods of computational physics. Readers will discover the benefits of numerical methods
for solving complex mathematical problems and for the direct simulation of physical processes.
The book is divided into two main parts: Deterministic methods and stochastic
methods in computational physics. Based on concrete problems, the first
part discusses numerical differentiation and integration, as well as the treatment of ordinary
differential equations. This is extended by a brief introduction to the numerics of partial differential equations. The
second part deals with the generation of random numbers, summarizes the basics of stochastics, and
subsequently introduces Monte-Carlo (MC) methods. Specific emphasis is on MARKOV chain MC algorithms. The
final two chapters discuss data analysis and stochastic optimization. All this is again motivated and
augmented by applications from physics. In addition, the book offers a number of appendices to provide the
reader with information on topics not discussed in the main text.
Numerous problems with worked-out solutions, chapter introductions and
summaries, together with a clear and application-oriented style support the
reader. Ready to use C++ codes are provided online.