Flow-based sampling for fermionic lattice field theories

Flow-based sampling for fermionic lattice field theories

Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact. In the context of lattice field theory, proof-of-principle studies have demonstrated the effectiveness of t...

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Bibliographic Details
Main Authors: Albergo, Michael S, Kanwar, Gurtej, Racanière, Sébastien, Rezende, Danilo J, Urban, Julian M, Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C, Shanahan, Phiala E
Other Authors: Massachusetts Institute of Technology. Center for Theoretical Physics
Format: Article
Language:English
Published: American Physical Society (APS) 2022
Online Access:https://hdl.handle.net/1721.1/142202

Internet

https://hdl.handle.net/1721.1/142202

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