Conference Paper/Proceeding/Abstract 6 views
Diffusion models learn distributions generated by complex Langevin dynamics
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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
Full text not available from this repository: check for access using links below.
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...
| Published in: | Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024) |
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| ISSN: | 1824-8039 |
| Published: |
Trieste, Italy
Sissa Medialab
2025
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| Online Access: |
Check full text
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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. |
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| 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 |