Abstract
Normalizing flows are a class of deep generative models that provide a promising route to sample lattice field theories more efficiently than conventional Monte Carlo simulations. In this work we show that the theoretical framework of stochastic normalizing flows, in which neural-network layers are combined with Monte Carlo updates, is the same that underlies out-of-equilibrium simulations based on Jarzynski’s equality, which have been recently deployed to compute free-energy differences in lattice gauge theories. We lay out a strategy to optimize the efficiency of this extended class of generative models and present examples of applications.
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Caselle, M., Cellini, E., Nada, A. et al. Stochastic normalizing flows as non-equilibrium transformations. J. High Energ. Phys. 2022, 15 (2022). https://doi.org/10.1007/JHEP07(2022)015
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DOI: https://doi.org/10.1007/JHEP07(2022)015