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Diffusion models learn distributions generated by complex Langevin dynamics
December 02, 2024 Β· Declared Dead Β· π arXiv.org
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Authors
Diaa E. Habibi, Gert Aarts, Lingxiao Wang, Kai Zhou
arXiv ID
2412.01919
Category
hep-lat
Cross-listed
cs.LG
Citations
3
Venue
arXiv.org
Last Checked
3 months ago
Abstract
The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process.
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