Ph 20. Computational Physics Laboratory I. 6 units (0-6-0); first term, 2025-26. Prerequisites: CS 1 or equivalent. The course introduces numerical methods and scientific programming for solving physics using Python. Topics include numerical integration, root finding, linear systems, and differential equations, all within the context of physical models. Emphasis is placed on developing reliable, readable code and interpreting numerical output in terms of physical behavior. Students are expected to already be familiar with working in a UNIX environment. This includes navigating directories, using the command line, managing Python virtual environments, and employing version control systems. These tools will be used throughout the course but will not be taught explicitly. Students must be able to install packages, execute scripts, and track code changes as part of their regular workflow.
Instructor: Adhikari

Ph 21. Computational Physics Laboratory II. 6 units (0-6-0); second term, 2025-26. Prerequisites: Ph 20. This course covers computational techniques for signal processing and weak signal detection in physics. Topics include Fourier transforms, filtering, power spectral density estimation, matched filtering, and introductory Bayesian methods. Students will develop tools to extract signals from noisy data and quantify the confidence of detection. Applications are drawn from gravitational wave detection, high-energy physics experiments, and quantum hypothesis testing. Emphasis is placed on practical data analysis and the construction of reliable, well-documented analysis pipelines for real or simulated datasets.
Instructor: Adhikari

Ph 22. Computational Physics Laboratory III. 6 units (0-6-0); third term, 2025-26. Prerequisites: Ph 20 + Ph 21. An introduction to machine learning techniques for modeling and analyzing physical systems. Topics include classification, regression, dimensionality reduction, and the construction of simple neural networks. Students will apply these methods to datasets from physics experiments and simulations, using machine learning to identify patterns, build predictive models, and work with high-dimensional data. The course also includes one reinforcement learning problem, focused on optimizing control in a basic physical system. Emphasis is placed on understanding when and how to apply ML techniques in physics, with attention to interpretability, overfitting, and the use of physical constraints in guiding learning-based approaches.
Instructor: Adhikari