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AI/ML for Fusion Summer School 2026

Schedule (June 1-12, 2026)

Schedule

[Pre-flight]

Intro to Modeling, Linear Regression (lecture slides)
Brief course on git
Exercises on git
Installing Visual Studio Code
Introduction to High Performance Computing

📚 Data Science Lectures and 💻 Tutorials (ongoing)

Lecture 1: Linear Models & Basic Recap of Matrix Operations
Lecture 1/Tutorial: Ordinary Least Squares
Lecture 2: Linear Classifiers
Lecture 2/ Tutorial: Linear Classifiers Perceptron
Lecture 3: Introduction to Deep Learning
Lecture 4: Introduction to Deep Learning: Network Optimization
Lecture 5: Decision Trees and XGBoost
Lecture5/Tutorial: Decision Trees
Lecture 6: Pytorch
Lecture6/Tutorial: Pytorch Basics
Lecture 6/Tutorial: Classification with Fusion Data
Lecture 7: Convolutional Neural Networks
Lecture 7/Tutorial: Convolutional Neural Networks
Lecture 8: Graph Neural Networks
Lecture 9: Generative AI (Part I)
Lecture 10: Lecture 9: Generative AI (Part II)
Lecture 11: Introduction to Transformers
Lecture 13: Introduction to Vision Transformers (Part I)
Lecture 14/Tutorial: Vision Transformer
Lecture 15: Introduction to Bayesian Statistics
Lecture 15/Tutorial: Bayes Nets
Lecture 15/Tutorial: Markov Chain Monte Carlo
Lecture 16: From Bayesian Regression to Gaussian Processes
Lecture 17: Bayesian Neural Networks
Lecture 17/Tutorial: Basic Bayesian Neural Network

👤💻🚀 Hands-On Sessions

Hands-on 1: Regression
Hands-on 2: Regression Fusion
Hands-on 3: Graph Neural Networks
Hands-on 4: Flow Matching Normalizing Flows

📚 Fusion Lectures (being updated during the summer school)

Introduction to Fusion (S. Mordijck)
Tokamak Operations: Pegasus (S. Diem)
Alcator C-Mod: Database Intro (A.R. Saperstein)
Plasma Diagnostics (E. Kostadinova)
Disruption Physics (A.R. Saperstein)
Edge Localized Modes (S. Mordijck)
Making Plasma Science Open (N. Murphy)
Managing Data (N. Cummings)
ML Uncertainty Quantification for the Experimental Fusion (C. Cowley)

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By Cristiano Fanelli

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AI4Fusion