37 citations · 75 across the 12 of their papers we have counts for
11 papers · 1 filter
Diffusion Models for Sampling Near Criticality in Lattice Field Theories
Yang-yang Tan, Gert Aarts, Diaa E. Habibi +2
We investigate generative diffusion models as denoising samplers for two- and three-dimensional lattice theory across the symmetric, near-critical, and broken phases. Validat…
Generalizable Equivariant Diffusion Models for Non-Abelian Lattice Gauge Theory
Gert Aarts, Diaa E. Habibi, Andreas Ipp +5
We demonstrate that gauge equivariant diffusion models can accurately model the physics of non-Abelian lattice gauge theory using the Metropolis-adjusted annealed Langevin algorith…
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…
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…
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda +4
The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from co…
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…
Generative Diffusion Models for Lattice Field Theory
Lingxiao Wang, Gert Aarts, Kai Zhou
This study delves into the connection between machine learning and lattice field theory by linking generative diffusion models (DMs) with stochastic quantization, from a stochastic…
Diffusion Models as Stochastic Quantization in Lattice Field Theory
Lingxiao Wang, Gert Aarts, Kai Zhou
In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ). The DM is realized by approximating the reversal of a sto…