TY  - JOUR
AU  - Albandea, D.
AU  - Del Debbio, L.
AU  - Hernandez, P.
AU  - Kenway, R.
AU  - Marsh Rossney, J.
AU  - Ramos, A.
PY  - 2023
DA  - 2023//
TI  - Learning trivializing flows
T2  - Eur. Phys. J. C
JO  - European Physical Journal C
SP  - 676 - 14pp
VL  - 83
IS  - 7
PB  - Springer
AB  - 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 L & uuml;scher. 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 f(4) 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.
SN  - 1434-6044
UR  - https://arxiv.org/abs/2302.08408
UR  - https://doi.org/10.1140/epjc/s10052-023-11838-8
DO  - 10.1140/epjc/s10052-023-11838-8
LA  - English
N1  - WOS:001066712500003
ID  - Albandea_etal2023
ER  -