9 papers
Physics-Conditioned Diffusion Models for Lattice Gauge Theory
Qianteng Zhu, Gert Aarts, Wei Wang +2
We develop diffusion models for simulating lattice gauge theories, where stochastic quantization is explicitly incorporated as a physical condition for sampling. We demonstrate the…
Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
Shuang Guo, Lingxiao Wang, Kai Zhou +1
The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning…
Combining complex Langevin dynamics with score-based and energy-based diffusion models
Gert Aarts, Diaa E. Habibi, Lingxiao Wang +1
Theories with a sign problem due to a complex action or Boltzmann weight can sometimes be numerically solved using a stochastic process in the complexified configuration space. How…
Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis
Ronghao Li, Sophia Han, Zidu Lin +3
The potential hadron-to-quark phase transition in neutron stars has not been fully understood as the property of cold, dense, and strongly interacting matter cannot be theoreticall…
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…
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…