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2022

Learning trivializing flows

Albandea, David, Del Debbio, Luigi, Hernández, Pilar et al.

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The recent introduction of machine learning techniques, especially normalizing flows, for the sampling of lattice gauge theories has shed some hope on improving the sampling efficiency of the traditional HMC algorithm.

Naive use of normalizing flows has been shown to lead to bad scaling with the volume.
In this talk we propose using local normalizing flows at a scale given by the correlation length.
Even if naively these transformations have a small acceptance, when combined with the HMC algorithm lead to algorithms with high acceptance, and also with reduced autocorrelation times compared with HMC.

Learning trivializing flows · Around

Built on

Trivializing maps, the wilson flow and the hmc algorithm
M. Lüscher · 2010
Earlier work this paper cites.
Testing trivializing maps in the hybrid monte carlo algorithm
Georg P. Engel and Stefan Schaefer · 2011
Earlier work this paper cites.

Similar

Flow-based generative models for markov chain monte carlo in lattice field theory
M. S. Albergo, G. Kanwar, and P. E. Shanahan · 2019
Cited alongside, same era.
Efficient modeling of trivializing maps for lattice ϕ \phi 4 theory using normalizing flows: A first look at scalability
Luigi Del Debbio, Joe Marsh Rossney, and Michael Wilson · 2021
Cited alongside, same era.

Then

Neural Network Field Transformation and Its Application in HMC
Xiao-yong Jin · 2022
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HMC with Normalizing Flows
Sam Foreman, Taku Izubuchi, Luchang Jin, Xiao-yong Jin, James C. Osborn, and Akio Tomiya · 2022
Closest in time.

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