Computational Physics

Catalog description

Develops tools for using computers to model the physical world. Uses examples pulled broadly from core areas of physics: Mechanics, Electricity and Magnetism, Statistical Mechanics and Thermodynamics, and Quantum Mechanics. Focus is on algorithmic thinking, converting mathematical representations into practical algorithms, working with data, and drawing physical conclusions from numerical results. Model problems will involve numerical quadratures, ordinary and partial differential equations, numerical linear algebra, event based simulations, and Monte Carlo techniques. May include modern techniques, such as those drawn from machine learning and artificial intelligence. Instruction will largely be in Julia, with computer labs integrated into lectures. No prior experience with Julia is necessary, but students should have some experience with programming. Graduate versions, PHYS 7680 and ASTRO 7690 , require an additional project which is not required in the undergraduate version, PHYS 4480 .