Equivariant Flow-Based Sampling for Lattice Gauge Theory.
Phys Rev Lett
; 125(12): 121601, 2020 Sep 18.
Article
en En
| MEDLINE
| ID: mdl-33016765
ABSTRACT
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that, at small bare coupling, the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as hybrid Monte Carlo and heat bath.
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Banco de datos:
MEDLINE
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En
Revista:
Phys Rev Lett
Año:
2020
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Article
País de afiliación:
Estados Unidos