Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theory

arXiv:2605.11199 · hep-lat, cs.LG · Submitted 2026-05-11 · Read on arXiv

hep-lat, cs.LG

Submitted: 2026-05-11

Updated: 2026-08-30

Comments: 18 pages, 6 figures

Code: https://github.com/qxxmax/operatormatching

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Flow Matching — 9× in this paper · explained in 8 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Normalizing Flow — 7× in this paper · explained in 3 episodes
• Normalizing Flows — 4× in this paper · explained in 4 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Hybrid Monte Carlo — 1× in this paper

Sources

• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• Diffusion Models as Stochastic Quantization in Lattice Field Theory
• Density estimation using Real NVP
• Group Equivariant Convolutional Networks

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