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Stochastic normalizing flows as non-equilibrium transformations
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DOI：10.1007/JHEP07(2022)015.png)
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摘要
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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.
Keyword:
Algorithms and Theoretical Developments
Lattice QCD
Other Lattice Field Theories
期刊
IF:
5.5
论文数:
3.9W
被引数:
13.7W
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