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- Title
Stochastic normalizing flows as non-equilibrium transformations.
- Authors
Caselle, Michele; Cellini, Elia; Nada, Alessandro; Panero, Marco
- 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.
- Subjects
NONEQUILIBRIUM flow; LATTICE field theory; LATTICE gauge theories; MONTE Carlo method
- Publication
Journal of High Energy Physics, 2022, Vol 2022, Issue 7, p1
- ISSN
1126-6708
- Publication type
Article
- DOI
10.1007/JHEP07(2022)015