Tuesday, 04 October 2022 at 14.30
Critical slowing down and topological freezing cause the Monte Carlo cost of lattice field theory simulations to severely diverge as the lattice regulator is removed. I will discuss the application of a generative machine learning method, namely "flow-based models", as a means of circumventing these issues without compromising exactness. The construction and evaluation of flow-based samplers in proof-of-principle gauge theory applications will be addressed. Finally, I highlight recent progress towards including the contributions of fermionic degrees of freedom in this method.