Diffusion models learn distributions generated by complex Langevin dynamics
This addresses a challenge in theoretical physics for researchers studying sign problems, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of understanding distributions generated by complex Langevin processes, which are hard to characterize, by using diffusion models to learn these distributions from data, demonstrating their effectiveness in this context.
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.