| Record |
Author  |
Albandea, D.; Del Debbio, L.; Hernandez, P.; Kenway, R.; Marsh Rossney, J.; Ramos, A. |
| Title |
Learning trivializing flows |
Type |
Journal Article |
| Year |
2023 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
| Volume |
83 |
Issue |
7 |
Pages |
676 - 14pp |
| Keywords |
|
| Abstract |
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. |
| Address |
[Albandea, D.; Hernandez, P.; Ramos, A.] Edificio Inst Invest, IFIC CSIC UVEG, Apt 22085, Valencia 46071, Spain, Email: david.albandea@uv.es |
| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1434-6044 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:001066712500003 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5698 |
| Permanent link to this record |
