AI Winter School 2026 - CFPU/Brown Department of Physics

Contribution List

1. Introductory Module

Shawn Dubey

Introductory Module

This module is the introductory module to the virtual AI Winter School. It will consist of a short introductory lecture to AI/ML and then a brief hands-on session that will introduce participants to the basics of using Google Colab, the required platform for this winter school. This module is particularly geared toward those with no knowledge of AI/ML and is recommended as a prerequisite to...

3. Module 1: Physics‑Informed Neural Networks: Teaching Models the Laws of Nature

Bryan Ostdiek

Module 1: Physics‑Informed Neural Networks: Teaching Models the Laws of Nature

This session introduces Physics‑Informed Neural Networks (PiNNs), a powerful approach that blends data‑driven learning with fundamental physical equations. We’ll explore how incorporating domain knowledge into the training process improves accuracy, reduces data requirements, and increases interpretability. Through intuitive examples and hands‑on exercises, participants will learn how PiNNs...

4. Module 2: Applications of Optimal Transport in Collider Physics

Matt LeBlanc (Brown University)

Module 2: Applications of Optimal Transport in Collider Physics

Optimal Transport (OT) has emerged as a powerful framework in high-energy physics that offers a geometrical language for data analysis. This module explores applications of OT to collider physics, focused on the "Energy-Mover's Distance." We will explore the metric space of particle physics data, and learn how many aspects of collider data analysis like jet clustering and event shape...

2. Module 3: Training Simulations to Predict the First Stars and their Effect

Jonathan Pober, Madhurima Choudhury

Module 3: Training Simulations to Predict the First Stars and their Effect

We are interested in regression tasks in analyzing 21 cm cosmology data. Most semi-numerical algorithms use a unique (and not always physical) parameterization of the 21 cm signal, but we can regress on well-defined phenomenological parameters. In this module, we’ll look at estimating the neutral fraction of the intergalactic medium from 21 cm signal maps.

5. Module 4: Object Detection for Rare Event Searches

Jeffrey Schueler (University of New Mexico)

Module 4: Object Detection for Rare Event Searches

Object deteciton is a cross-disciplinary computer vision technique where a model is trained to simultaneously classify and localize objects of interest in image data. This module introduces the notion of object detection and how the MIGDAL experiment uses it to enable real data training in its search for the Migdal effect. While we use simulation in this module, participants will be given...

6. Module 5: Denoising Electron Densities from Stochastic Simulations of Electronic Structure Using AI

Brenda Rubenstein (Brown)

Module 5: Denoising Electron Densities from Stochastic Simulations of Electronic Structure Using AI

One of the most computationally-efficient approaches for modeling solids that feature strong electron correlation is by using Quantum Monte Carlo techniques. These techniques are among the most accurate available, but importantly, scale gracefully with system size because of their use of random sampling. Nonetheless, this random sampling can make the measurement of observables such as the...

7. Module 6: Reinforcement Learning for Orbital Transfers

James Verbus

Module 6:

Using a simple 2D orbital transfer as a testbed, we’ll compare an analytic Hohmann solution to a controller learned from trial-and-error in simulation. You’ll build the RL setup from scratch (state/action/reward), train a PPO policy, and develop intuition by inspecting the learned trajectories, trade-offs, and characteristic failure cases.