Generative Models for Lattice Field Theory

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 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 sampling approaches such as Hamiltonian/hybrid Monte Carlo in this context, and discuss the potential impacts of these approaches in nuclear and particle physics.