Stochastic Normalizing Flows for Lattice Field Theory

Normalizing Flows (NFs) are a class of deep generative models that have recently been proposed as efficient samplers for Lattice Field Theory. Although NFs have demonstrated impressive performance in toy models, their scalability to larger lattice volumes remains a significant challenge, limiting their application to state-of-the-art problems. A promising approach to overcoming these scaling limitations involves combining NFs with non-equilibrium Markov Chain Monte Carlo (NEMCMC) algorithms, resulting in Stochastic Normalizing Flows (SNFs). SNFs harness the scalability of MCMC samplers while preserving the expressiveness of NFs. In this seminar, I will introduce the concepts of NEMCMC and NFs, demonstrate their combination into SNFs, and outline their connections with non-equilibrium thermodynamics. I will conclude by discussing key aspects of SNFs through their application to Effective String Theory, SU(3) gauge theory, and conformal field theory.