Deep Learning (AI) + Deep Thinking (Physics) = Deeper Understanding

The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, pronounced /aɪ-faɪ/) is one of the inaugural NSF AI research institutes. The IAIFI is advancing physics knowledge – from the smallest building blocks of nature to the largest structures in the Universe – and galvanizing AI research innovation. The IAIFI is a collaboration of both physics and AI researchers at MIT, Harvard, Northeastern, and Tufts. By combining state-of-the-art research with early career talent and a growing AI + physics community in the Boston area and beyond, the IAIFI is enabling researchers to develop AI technologies to tackle some of the most challenging problems in physics, and transfer these technologies to the broader AI community.

Repositories for IAIFI products:

• E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification, Sneh Pandya, Purvik Patel, Franc O, Jonathan Blazek
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• A Compound Poisson Generator approach to Point-Source Inference in Astrophysics, Gabriel H. Collin, Nicholas L. Rodd, Tyler Erjavec, Kerstin Perez
• Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers, Paul Lutkus, Taritree Wongjirad, Schuchin Aeron
• E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once, Benjamin Nachman and Jesse Thaler
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge, Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, Philip Harris
• Enhancing searches for resonances with machine learning and moment decomposition, Ouail Kitouni, Benjamin Nachman, Constantin Weisser, and Mike Williams
• Poisson Flow Generative Models, Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola
• Neural Descriptor Fields: SE(3) Equivariant Object Representations for Manipulation, Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann
• Learning Task Informed Abstractions, Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola