Distinguished Seminar in Computational Science and Engineering

Distinguished Seminar in Computational Science and Engineering December 3, 2026, 12-1PM 45-432 in Building 45 and Zoom Webinar Generative sampling of high-dimensional probability distributions for lattice field theory studies of the structure of matter Phiala Shanahan Professor of Physics, MIT Department of Physics Abstract: As machine learning algorithms continue to enable and accelerate physics calculations, the development of problem-specific, physics-informed machine learning approaches is becoming increasingly sophisticated, impactful, and important. Lattice quantum field theory provides a particularly demanding setting: algorithms must sample extremely high-dimensional distributions at scale while respecting exact mathematical and physical constraints. I will describe recent advances in generative modelling emerging from the challenge of exact sampling from known probability distributions in the context of lattice quantum field theory calculations in particle and nuclear physics. I will discuss, in particular, flow-based generative models, explain the importance of guarantees of exactness, and show how complex symmetries—such as gauge symmetry—can be incorporated directly into model architectures. These developments connect questions in lattice field theory with broader challenges in scalable sampling, structure-preserving computation, and scientific machine learning. Bio: Shanahan is a professor in the Center for Theoretical Physics — A Leinweber Institute at the Massachusetts Institute of Technology and the deputy director of the NSF Institute for Artificial Intelligence and Fundamental Interactions. She grew up in Adelaide, Australia, and obtained her B.Sc. and Ph.D. from the University of Adelaide in 2012 and 2015, respectively. She joined the MIT faculty in 2018 after a postdoctoral position at MIT and a faculty position at the College of William & Mary and Thomas Jefferson National Accelerator Laboratory.