Special Topics in Computational Physics (PHYS*7730)

Code and section: PHYS*7730*01

Term: Fall 2021

Instructor: Alex Gezerlis




Alex Gezerlis
MacN 219

Office Hours: By appointment.

Tuesday & Thursday 1:00 pm – 2:20 pm


Zoom (see courselink for more info)

Course Materials

Required textbook

  • A. Gezerlis, Numerical Methods in Physics with Python (Cambridge University Press, 2020)
  • See also the companion website: https://numphyspy.org 

Lecture Content

This is a special-topics course on what is known as computational science or scientific computing. We will focus on the interplay between science problem, mathematical formulation, and computational implementation. Previous exposure to Python programming is required. My current plan is to discuss selected aspects of:

  • Floating-point numbers
  • Automatic differentiation
  • Eigenproblems and the SVD
  • Multidimensional minimization
  • The fast Fourier Transform
  • Nonlinear regression
  • Gauss-Legendre quadrature
  • The Metropolis algorithm
  • ODEs and PDEs

Expected Background

I expect that all students will have some familiarity with basic numerical methods (e.g., Gaussian elimination, Simpson’s rule, Euler’s method), typically provided in an undergraduate course on computational physics. Programming-related examples and assignments will be in Python, but I will not cover basic programming in the lectures. This is a graduate course, so the assignments will be correspondingly challenging.


The course will involve homework assignments (some of which will not be graded) and a scheduled final exam.