https://doi.org/10.1140/epjc/s10052-023-11838-8
Regular Article - Theoretical Physics
Learning trivializing flows
1
IFIC (CSIC-UVEG), Edificio Institutos Investigación, Apt. 22085, 46071, Valencia, Spain
2
Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, EH9 3FD, Edinburgh, UK
Received:
19
April
2023
Accepted:
14
July
2023
Published online:
28
July
2023
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 hybrid Monte Carlo (HMC) algorithm. In this work we study a modified HMC algorithm that draws on the seminal work on trivializing flows by Lüscher. Autocorrelations are reduced by sampling from a simpler action that is related to the original action by an invertible mapping realised through Normalizing Flows models with a minimal set of training parameters. We test the algorithm in a 
theory in 2D where we observe reduced autocorrelation times compared with HMC, and demonstrate that the training can be done at small unphysical volumes and used in physical conditions. We also study the scaling of the algorithm towards the continuum limit under various assumptions on the network architecture.
© The Author(s) 2023
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