Accelerating lattice field theory simulations using machine learning

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.