The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.
Prerequisites: Recommended preparation: Python, C, C++, Unix.
Grading Basis: Graded
Introduction to computational physics, including programming in C, C++, and Python. Topics include numerical integration of ordinary differential equations, finite differences and stability analysis, numerical solution of partial differential equations (e.g., the Schroedinger and diffusion equations) in more than one dimension, Krylov space methods (e.g., eigensystem solvers and matrix inversion), and Monte Carlo integration. Introductory machine learning and high-performance computing methods may be covered. Writing code to solve current problems from selected areas of physics and astrophysics.
No classes found.