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 …

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 …

Fixed point lattice actions are designed to have continuum classical properties unaffected by
discretization effects and reduced lattice artifacts at the quantum level. They provide a …

We develop diffusion models for lattice gauge theories which build on the concept of
stochastic quantization. This framework is applied to $ U (1) $ gauge theory in $1+ 1 …

This is a pedagogical review of the possible connection between the stochastic quantization
in physics and the diffusion models in machine learning. For machine-learning applications …

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 …

Score-based diffusion models have proven effective in image generation and have gained
widespread usage; however, the underlying factors contributing to the performance disparity …

It has been proposed that random wide neural networks near Gaussian process are
quantum field theories around Gaussian fixed points. In this paper, we provide a novel map …

A novel point cloud diffusion model for relativistic heavy-ion collisions, capable of ultra-fast
generation of event-by-event collision output, is introduced. When trained on UrQMD …

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 …