Numerical Methods in Physics

Most of the physics you've solved analytically so far has been carefully chosen to have a closed-form answer; this course is about what you actually do when the integral, the ODE, or the eigenvalue problem doesn't cooperate, and you have to discretize, iterate, and trust a computer instead. You'll spend the semester writing code (typically Python) to handle roots, linear systems, FFTs, ODEs and PDEs, Monte Carlo, and molecular dynamics, building up to a project where you simulate a real physical system and defend the numbers you got. It's the bridge between your math-methods courses and any computational work later — research projects, lab data analysis, or graduate-level statistical and condensed-matter problems — and it's also where you learn that "the answer" is inseparable from the error bar around it.