Diffusion models learn distributions generated by complex Langevin dynamics
Diaa Eddin Habibi, Gert Aarts, Lingxiao Wang, Kai Zhou · 2025
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.