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UCSD PHYS 142/242: Computational Physics — Stochastic Methods and Path Integrals (Fall 2026)
Week 0
Syllabus
Lecture 01: Course overview, the double-slit experiment, and how this course handles AI
Week 1
Lecture 02: Quantum mechanics refresher: states, operators, and the propagator
Lab 01: Python, Jupyter, and DataHub (Slides)
Python Introduction (Reference Notebook)
Lecture 03: Feynman path integral
Lecture 04: Feynman path integral (continued)
Numba and ffmpeg (Reference Materials)
Lab 02: Numba
Lab 02: ffmpeg
Lab 01: Python, Jupyter, and DataHub
Lab 02: NumPy, SciPy, and Matplotlib
Week 2
Lecture 05: Free particle
Lecture 06: Harmonic oscillator
Lab 03: Random Numbers, Seeding, and Reproducible Simulation
Lab 04: Git and GitHub
Lecture 07: Schrödinger equation
Week 3
Assignment 01 (Due Mon 10/19)
Lecture 08: Unitarity and the propagator trace
Lab 05: Assignment 1 Tutorial
Lecture 10: Double well potential
Week 4
Lecture 11: The eigenvalue problem as a validation tool (with demo)
Lecture 11: Eigenvalue Problem Demo
Which points count?
Week 5
Assignment 02 (Due Wed 11/4)
Lab 09: Assignment 2 Tutorial
Lab 10: Auditing AI-Generated Numerical Code
Week 6
Lab 11: MCMC and the 2D Ising Model
Lab 12: Statistical Error Estimation
Week 7
Lab 14: Path-Integral MCMC Tutorial
Week 8
Lab 16: VEGAS Tutorial
Repository
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Index
