PoS - Proceedings of Science
Volume 430 - The 39th International Symposium on Lattice Field Theory (LATTICE2022) - Algorithms
Stochastic normalizing flows for lattice field theory
M. Caselle, E. Cellini*, A. Nada and M. Panero
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Pre-published on: December 06, 2022
Published on:
Abstract
Stochastic normalizing flows are a class of deep generative models that combine normalizing flows with Monte Carlo updates and can be used in lattice field theory to sample from Boltzmann distributions. In this proceeding, we outline the construction of these hybrid algorithms, pointing out that the theoretical background can be related to Jarzynski's equality, a non-equilibrium statistical mechanics theorem that has been successfully used to compute free energy in lattice field theory. We conclude with examples of applications to the two-dimensional $\phi^4$ field theory.
DOI: https://doi.org/10.22323/1.430.0005
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