Aspects of scaling and scalability for flow-based sampling of lattice QCD

Aspects of scaling and scalability for flow-based sampling of lattice QCD

Abstract Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains t...

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Bibliographic Details
Main Authors: Abbott, Ryan, Albergo, Michael S., Botev, Aleksandar, Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C., Matthews, Alexander G. D. G., Racanière, Sébastien, Razavi, Ali, Rezende, Danilo J., Romero-López, Fernando, Shanahan, Phiala E., Urban, Julian M.
Other Authors: Massachusetts Institute of Technology. Center for Theoretical Physics
Format: Article
Language:English
Published: Springer Berlin Heidelberg 2023
Online Access:https://hdl.handle.net/1721.1/152908

Internet

https://hdl.handle.net/1721.1/152908

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