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AI/ML for Nuclear Physics HUGS 2025

Lectures

(Lec 1, part A) A Brief Introduction to AI/ML
(Lec 1, part B) AI/ML in Nuclear Physics
(Lec 2) CNN for Physics by K. Suresh
(Lec 3) Multi-Objective Optimization by K. Suresh

Tutorials

(Lec 1) Deeply Learning Deep Inelastic Scattering
(Lec 2) CNN - Part 1
(Lec 2) CNN - Part 2
(Lec 2) Dataset
(Lec 3) Design with Multi-Objective Bayesian Optimization
(Lec 3) Design with Multi-Objective Evolutionary Optimization

(Old) Lectures

(Lec 1) AI/ML for Nuclear Physics - A High-Bias, Low-Variance Introduction
(Lec 2) Leveraging AI/ML for the future Electron Ion Collider
(Lec 2, extra) AI/ML activities at Jefferson Lab - Paving the way for the Electron Ion Collider
(Lec 3) ePIC at EIC – The First Large-Scale Experiment Designed with the AI Assistance

(Old) Tutorials

(Tut 1) "Pre-flight"
(Tut 2 - part a) AI-assisted design with MOEA
(Tut 2 - part b) AI-assisted design with MOBO
AI4EIC tutorials

[Pre-flight]

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

Other References

Introduction to Data Science (beginners)
Interactively Navigate the Pareto Front of the EIC Tracker
EIC Overview

Datasets / Modules

(Lec 1) all-h1-rapgap.root
(Lec 1) intro_Data.py
(Lec 2) Dataset

Additional resources

References

Repository
Open issue

Index

By Cristiano Fanelli

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HUGS2025