Numerical Methods in Physics with Python

A standalone text on computational physics combining idiomatic Python, foundational numerical methods, and physics applications.

Alex Gezerlis (Author)

9781009303866, Cambridge University Press

Paperback / softback, published 20 July 2023

700 pages
25.4 x 17.8 x 3.6 cm, 1.296 kg

'Gezerlis' text takes a venerable subject - numerical techniques in physics - and brings it up to date and makes it accessible to modern undergraduate curricula through a popular, open-source programming language. Although the focus remains squarely on numerical techniques, each new lesson is motivated by topics commonly encountered in physics and concludes with a practical hands-on project to help cement the students' understanding. The net result is a textbook which fills an important and unique niche in pedagogy and scope, as well as a valuable reference for advanced students and practicing scientists.' Brian Metzger, Columbia University

Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.

Preface
1. Idiomatic Python
2. Numbers
3. Derivatives
4. Matrices
5. Zeroes and minima
6. Approximation
7. Integrals
8. Differential equations
Appendix A. Installation and setup
Appendix B. Number representations
Appendix C. Math background
Bibliography
Index.

Subject Areas: Computer science [UY], Programming & scripting languages: general [UMX], Maths for scientists [PDE], Mathematical modelling [PBWH]