Acceleration of Monte Carlo for lattice field theory with machine-learned flow models

I will discuss recent progress in accelerating Monte Carlo sampling for lattice field theory using machine-learned flow models. In particular, I will discuss applications where correlated ensembles of configurations can be generated and exploited to achieve significantly reduced variance in observables of interest in particle and nuclear physics.