An Introduction to Computational Physics
3rd edition
, University of Nevada, Las Vegas
Coming soon
Online ISBN: 9781009718806
Expected online publication date:
January 2027
Hardback ISBN: 9781009718813
Expected Hardback publication date: 30 November 2026
Paperback ISBN: 9781009718837
Expected Paperback publication date: 30 November 2026
- Textbook
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.
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.
