Course Synopses

 Computational Physics: Algorithms and Applications

Prerequisite: Permission Undergraduate Program Director This email address is being protected from spambots. You need JavaScript enabled to view it.

This course introduces algorithmic concepts and familiarizes students with fundamental computational tools that are essential for students in STEM and related fields. The course emphasizes practical applications and prepares students for both academic and industrial pursuits. Modern computational approaches, including Python numpy/scipy libraries and Jupyter notebooks, will be utilized to develop a strong foundation in computational physics. The course covers a range of algorithms, including solving partial differential equations, large-scale minimization problems, high-dimensional integration problems using Monte Carlo methods, linear and logistic regression, and provides an introduction to deep learning and neural networks.

Prerequisites: Basic knowledge of calculus, physics, and some programming skills (preferably in Python). This course is appropriate for first-year students in their second semester planning to major in physics or astrophysics.

Regular assignments and coding exercises. Class participation and engagement.