Abbott, Ryan; Albergo, Michael S.; Botev, Aleksandar; Boyda, Denis; Cranmer, Kyle; Hackett, Daniel C.; Kanwar, Gurtej; Matthews, Alexander G. D. G.; Racanière, Sébastien; Razavi, Ali; Rezende, Danilo; Romero-López, Fernando; Shanahan, Phiala; Urban, Julian M. (9 January 2022). Sampling QCD field configurations with gauge-equivariant flow models. In: The 39th International Symposium on Lattice Field Theory (036). Trieste, Italy: Sissa Medialab 10.22323/1.430.0036
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Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gauge field configurations in simple lattice field theories. A critical question is whether this success will translate to studies of QCD. This Proceedings presents a status update on advances in this area. In particular, it is illustrated how recently developed algorithmic components may be combined to construct flow-based sampling algorithms for QCD in four dimensions. The prospects and challenges for future use of this approach in at-scale applications are summarized.
Item Type: |
Conference or Workshop Item (Paper) |
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Division/Institute: |
08 Faculty of Science > Institute of Theoretical Physics 10 Strategic Research Centers > Albert Einstein Center for Fundamental Physics (AEC) |
UniBE Contributor: |
Kanwar, Gurtej Singh |
Subjects: |
500 Science > 530 Physics |
Publisher: |
Sissa Medialab |
Language: |
English |
Submitter: |
Franziska Stämpfli |
Date Deposited: |
21 Dec 2023 12:21 |
Last Modified: |
21 Dec 2023 12:21 |
Publisher DOI: |
10.22323/1.430.0036 |
BORIS DOI: |
10.48350/190365 |
URI: |
https://boris.unibe.ch/id/eprint/190365 |
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