This classic textbook, thoroughly revised and updated for its third edition, introduces the basic methods of computational physics. Clear, concise and practical, the new edition includes an additional chapter on machine learning and is supported with sample programs in Python. First, readers are presented with the numerical techniques that every computational scientist should have in their toolbox, including approximation of functions, numerical calculus, differential and partial differential equations, spectral analysis, linear algebra and matrix operations. The author then provides self-contained introductions to the research areas of molecular dynamics, fluid dynamics, Monte Carlo simulations, genetic algorithms and machine learning. Important concepts are illustrated with relevant examples, and each chapter concludes with a selection of exercises. Suitable for upper-division undergraduate to graduate courses on computational physics and scientific computing, this book is also a useful resource for anyone interested in using computation to solve scientific problems.
