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 …

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 …

Discrete diffusion models have gained increasing attention for their ability to model complex
distributions with tractable sampling and inference. However, the error analysis for discrete …

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 …

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-based generative models represent a forefront direction in generative artificial
intelligence (AI) research today. Recent studies in physics have suggested that the …

Diffusion models are currently the leading generative AI approach used for image
generation in eg DALL-E and Stable Diffusion. In this talk we relate diffusion models to …

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 …

Lattice gauge-equivariant convolutional neural networks (L-CNNs) can be used to form
arbitrarily shaped Wilson loops and can approximate any gauge-covariant or gauge …

The last decade has seen an explosive growth of interest in exploiting developments in
machine learning to accelerate lattice QCD calculations. On the sampling side, generative …