Equivariant Flow-Based Sampling for Lattice Gauge Theory
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...
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Bibliographic Details
| Main Authors: |
Kanwar, Gurtej,
Albergo, Michael S,
Boyda, Denis,
Cranmer, Kyle,
Hackett, Daniel C,
Racanière, Sébastien,
Rezende, Danilo Jimenez,
Shanahan, Phiala E |
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| Other Authors: |
Massachusetts Institute of Technology. Center for Theoretical Physics |
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| Format: | Article
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| Language: | English |
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| Published: |
American Physical Society (APS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/134400
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