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Diffusion models learn distributions generated by complex Langevin dynamics

Diaa Eddin Habibi, Gert Aarts Orcid Logo, Lingxiao Wang, Kai Zhou

Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024), Volume: 466, Start page: 039

Swansea University Authors: Diaa Eddin Habibi, Gert Aarts Orcid Logo

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DOI (Published version): 10.22323/1.466.0039

Abstract

The probability distribution effectively sampled by a complex Langevin process for theories witha sign problem is not known a priori and notoriously hard to understand. Diffusion models, a classof generative AI, can learn distributions from data. In this contribution, we explore the ability ofdiffus...

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Published in: Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024)
ISSN: 1824-8039
Published: Trieste, Italy Sissa Medialab 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69013
Abstract: The probability distribution effectively sampled by a complex Langevin process for theories witha sign problem is not known a priori and notoriously hard to understand. Diffusion models, a classof generative AI, can learn distributions from data. In this contribution, we explore the ability ofdiffusion models to learn the distributions created by a complex Langevin process.
College: Faculty of Science and Engineering
Funders: DEHis supportedby theUKRIAIMLACCDTEP/S023992/1. GAis supportedbySTFCConsolidatedGrantST/X000648/1.KZissupportedbytheCUHK-Shenzhen Universitydevelopment fundundergrantNo.UDF01003041andUDF03003041, andShenzhen PeacockfundunderNo.2023TC0179.
Start Page: 039

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