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.

Stochastic normalizing flows as non-equilibrium transformations

Caselle, Michele;Cellini, Elia;Nada, Alessandro
;
Panero, Marco
2022-01-01

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.
2022
2022
7
015/00
015/30
https://link.springer.com/article/10.1007/JHEP07(2022)015
Algorithms and Theoretical Developments; Lattice QCD; Other Lattice Field Theories;
Caselle, Michele; Cellini, Elia; Nada, Alessandro; Panero, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1868558
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