November 30, 2020 to December 3, 2020
Europe/Dublin timezone

Machine learning for sampling in lattice field theory

Nov 30, 2020, 1:30 PM


Phiala Shanahan


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.

Primary author

Phiala Shanahan

Presentation materials