Numerical Methods in Physics with Python

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

Alex Gezerlis (Author)

9781009303859, Cambridge University Press

Hardback, published 20 July 2023

706 pages
26.2 x 18.4 x 4.3 cm, 1.48 kg

'… an excellent example of a textbook built on long and established teaching experience … the author has successfully achieved his goals: [this] is an interesting book that has its main appeal in the wealth of examples, the projects proposed, and the Python codes. … a very useful and interesting book that I will certainly include in the material I use for my lectures on numerical methods.' Gabriele Ciaramella, SIAM Review

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]