Paper ID: 2412.01919

Diffusion models learn distributions generated by complex Langevin dynamics

Diaa E. Habibi, Gert Aarts, Lingxiao Wang, Kai Zhou

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.

Submitted: Dec 2, 2024