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arXiv · 2412.01919

Diffusion models learn distributions generated by complex Langevin dynamics

Diaa E. Habibi·Gert Aarts·Lingxiao Wang·Kai Zhou
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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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Diaa E. Habibi, Gert Aarts, Lingxiao Wang, Kai Zhou. 2024-12-02. Diffusion models learn distributions generated by complex Langevin dynamics. https://arxiv.org/abs/2412.01919

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