Speaker
Description
Critical slowing down is the well-known phenomenon whereby the integrated autocorrelation time of Markov Chain Monte Carlo simulations, and therefore the computational cost, grows rapidly as one approaches the continuum limit. This effect is particularly severe for topological observables in non-Abelian gauge theories.
In this talk, I will present Stochastic Normalizing Flows (SNFs), hybrid algorithms in which non-equilibrium Markov Chain Monte Carlo algorithms are enhanced with normalizing flows, a class of deep generative models. I will show that SNFs significantly mitigate topological freezing in $3+1$ dimensional SU(3) gauge theory, while exhibiting favorable scaling with the lattice volume.
I will then introduce a novel neural enhanced out-of-equilibrium approach for sampling lattice field theory based on Langevin dynamics. I will discuss the conceptual advantages of this framework and present preliminary results for lattice non-linear $\sigma$ models.