Computational Physics

Catalog description

Builds on knowledge of analytical mathematical tools by discussing how they are implemented and used in practice in numerical calculations for physics problems. Topics include integration, differentiation, linear and nonlinear equations, eigenvalue equations, linear algebra, Fourier transforms, ordinary and partial differential equations, and Monte Carlo and other stochastic methods. Applications will be drawn from classical mechanics, electricity and magnetism, quantum mechanics, and statistical mechanics, as applied to various physics research areas. Programming is done in Python. Students do a final project applying a method from class to a problem of their choice.