81 citations · 275 across the 19 of their papers we have counts for
5 papers · 1 filter
Diffusion models and stochastic quantisation in lattice field theory
Gert Aarts, Lingxiao Wang, Kai Zhou
Diffusion models are currently the leading generative AI approach used for image generation in e.g. DALL-E and Stable Diffusion. In this talk we relate diffusion models to stochast…
Diffusion models learn distributions generated by complex Langevin dynamics
Diaa E. Habibi, Gert Aarts, Lingxiao Wang +1
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…
Diffusion models for lattice gauge field simulations
Qianteng Zhu, Gert Aarts, Wei Wang +2
We develop diffusion models for lattice gauge theories which build on the concept of stochastic quantization. This framework is applied to gauge theory in dimensions.…
On learning higher-order cumulants in diffusion models
Gert Aarts, Diaa E. Habibi, Lingxiao Wang +1
To analyse how diffusion models learn correlations beyond Gaussian ones, we study the behaviour of higher-order cumulants, or connected n-point functions, under both the forward an…
Building imaginary-time thermal field theory with artificial neural networks
Tian Xu, Lingxiao Wang, Lianyi He +2
In this study, we introduce a novel approach in quantum field theories to estimate the action using the artificial neural networks (ANNs). The estimation is achieved by learning on…