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Data Free Training of Diffusion Models for Lattice Field Theories

Paper recorded by Signals 4 on 2026-10-13 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-13 on arXiv · recorded by Signals 4 on 2026-10-14

Category: cs.LG · 机器学习 · first seen 2026-10-14

Abstract

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 samples from the target distribution, seemingly reintroducing the original sampling p

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#287 most recent of 749 cs.LG papers we have recorded · ↑ newer: Cepheid: Decoupling Scale from Structure for Cross-Family Motion Compl · ↓ older: Exact Topological Compliance: Generating Persistence-Equivalent Graphs
Cite this page: Data Free Training of Diffusion Models for Lattice Field Theories: the #287 most recent of 749 cs.LG papers we have recorded (as of 2026-10-13). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/data-free-training-of-diffusion-models-for-lattice-field-theories.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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