Abstract: In the context of lattice quantum field theory calculations in particle and nuclear physics, I will describe avenues to accelerate sampling from known probability distributions using machine learning. I will focus in particular on flow-based generative models, and describe how guarantees of exactness and the incorporation of complex symmetries (e.g., gauge symmetry) into model architectures can be achieved. I will show the results of proof-of-principle studies that demonstrate that sampling from generative models can be orders of magnitude more efficient than traditional Hamiltonian/hybrid Monte Carlo approaches in this context.
Biography: Phiala Shanahan obtained her BSc and PhD from the University of Adelaide in 2012 and 2015. Before joining the MIT physics faculty in 2018, Prof. Shanahan was a Postdoctoral Associate at MIT and held a joint position as Assistant Professor at the College of William & Mary and Senior Staff Scientist at the Thomas Jefferson National Accelerator Facility. Prof. Shanahan is the recipient of Early-Career Awards from both the NSF and the DOE, and in 2021 was awarded the Maria Goeppert Mayer Award of the American Physical Society.
