AI/ML for Fusion Summer School 2025

The Open and Fair Fusion for Machine Learning Applications Project

(June 2-13, 2025)

This is the landing page of the AI4Fusion Summer School held at William & Mary during Summer 2025

Important

Applications are now open. Please submit this Google Form

Synopsis: An intensive 2-week summer school focused on undergraduate students with backgrounds in physics, engineering, computer science, applied mathematics and data science will be offered at William & Mary. This summer course will include a close to equal distribution of traditional instruction and active projects. The traditional instruction will provide daily 80 min instruction in morning classes with a focus on computing, applied mathematics, machine learning and fusion energy. These classes will be based on existing classes offered in data science at W&M, such as databases, applied machine learning and deep learning, Bayesian reasoning in data science. These classes will be supplemented with a class focused on fusion energy for the applications the students will tackle during the hands-on component and for students’ summer research. A draft agenda to be posted soon.

This course is based on the following references: [BGC17, KK22, RLM22]

[Pre-flight]

• Intro to Modeling, Linear Regression (lecture slides)
• 1. Brief course on git
  • 1.1. Version Control System (VCS)
  • 1.2. What is Git?
  • 1.3. Configuring your git
• 2. Exercises on git
  • 2.1. Exercise 1: Basic Git Commands
  • 2.2. Exercise 2: Commits, push and branches
  • 2.3. Advanced exercises
  • 2.4. GitHub Actions and Workflow Tutorial
• 3. Installing Visual Studio Code
• 4. Installing Extensions
• 5. Signing up and Installing GitHub Copilot
• 6. Introduction to High Performance Computing
  • 6.1. High Performance Computing
  • 6.2. SSH into a Front-End Node Using Proxy Jump
  • 6.3. William & Mary Research Computing: File Systems Tutorial
  • 6.4. Introduction to SLURM

Additional resources

• References

Credits: Material on git, VS-Code, and HPC from AID2E