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#machine learning Preprint Oct 2026

Data Free Training of Diffusion Models for Lattice Field Theories

Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce independent configurations. Diffusion models are promising for this purpose, but their standard denoising score matching objective requires sampl...

Dennis Grinwald, A. Singha, Khaled Kahouli et al. · 0 citations
Preprint Sep 2026

Sector-Resolved Flow Sampling for Topologically Frozen Lattice Gauge Theories

Topological fluctuations are essential to nonperturbative gauge theories but become increasingly difficult to sample toward the continuum limit, where Markov chains can freeze in sectors of fixed topological charge. We introduce a generative sampler, a mixture of sector-resolved samplers (MSRS) that explicitly resolves...

A. Singha, Jacob R. Kauffmann, K. Jansen et al. · 1 citation · ⚡1
Preprint Sep 2026

Multilevel Plaquette-Space Sampling for Lattice Gauge Theories with Local Constraint Solves

Lattice gauge theories are an important class of physical models with high-dimensional structured distributions that underpin first-principles calculations in particle, nuclear, and condensed-matter physics. Recent advances in generative modeling have opened new avenues for sampling Boltzmann distributions in lattice g...

A. Singha, Jacob Finkenrath, K. Jansen et al. · 0 citations

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