Machine Learning Applications in Physics
PHY5006 / PHYG004 · Sogang University · 2026 Spring
Course Information
| Instructor | Young Woo Choi |
| Course | PHY5006 (Graduate) / PHYG004 (Undergraduate) |
| Schedule | Wed, Fri 09:00 – 10:15 |
| Office | F303C |
| Office Hours | Mon, Tue, Thu 10:00 – 12:00 (by appointment) |
| ywchoi02@sogang.ac.kr |
Lectures
| # | Topic | Materials |
|---|---|---|
| 01 | Introduction to ML in Physics Physics vs ML modeling / JAX basics / Autodiff | Slides Notebook |
| 02 | Why Machine Learning in Physics? Approximation / Inference / Surrogate modeling | Slides Notebook |
| 03 | Neural Networks Basics Linear models / MLP / Activation functions | Slides Notebook |
| 04 | Linear Models Linear regression / Classification / Regularization | Notebook |
| 05 | Multilayer Perceptrons MLP implementation / Training loop / Optimization | Notebook |
| 06 | Convolutional Neural Networks Convolution / Pooling / LeNet / Ising phase classification | Notebook |
| 07 | FNN/CNN Applications in Physics Ising classification / Galaxy morphology / AI-assisted coding | Handout Solution |
| 08 | Automatic Differentiation & Physics-Informed Neural Networks Autodiff / JAX grad / PINN / Schrödinger equation | Lecture Handout Solution |
| 09 | microgpt: a GPT in 200 lines Transformer / Autograd / Attention / Adam / Karpathy microgpt | Lecture Slides Notebook Handout Solution |
| 11 | Graph Neural Networks for Molecules Graphs / MPNN / SchNet / Tetris / Chirality | Notebook |
| 12 | Equivariant Networks with e3nn-jax Equivariance / Spherical harmonics / Irreps / Tensor product / TFN | Notebook Handout KO Handout EN |
| 13 | Production-Level Machine Learning Potentials Behler-Parrinello / SchNet / NequIP / MACE / Allegro / Foundation potentials | Notebook |
| 14 | Generative Models I — Variational Autoencoders (VAE) Generative models / VAE / Latent space / ELBO / β-VAE | Notebook |
Important Dates
| Week | Event |
|---|---|
| Week 7 | Individual Presentations (3-minute oral) |
| Week 8 | Midterm week — no exam |
| Week 15 | Term Project Presentations |
| Week 16 | Final exam week — no exam |
Projects & Presentations
Individual Presentation
Week 7 · 30% of grade
- 3-minute oral presentation, with or without slides
- Propose an idea for applying ML to a physics problem
- Include motivation, methods, and expected outcomes
- Implementation is not required
Focus on:
- What is the physics question?
- Why ML?
- Inputs / outputs
- Expected gain over traditional methods
Term Project
Week 15 · 50% of grade
- Team-based, up to 3 students per team
- Apply machine learning to a physics problem
- Reproduce results from research papers, or conduct related exploratory studies
- Present your work in a team presentation
Grading
- Individual presentation: 30%
- Term project: 50%
- Participation: 20%