用扩散模型加速规范场论采样，避免临界慢化问题。
Sampling the Schwinger Model with Gauge-Equivariant Diffusion
- 构建U(1)等变得分模型，直接生成规范链配置。
- 可观测量估计与MCMC结果一致，且在临界参数下拓扑冻结减少。
- 适合研究格点规范场论中采样效率的科研人员。
我们首次研究了基于扩散的方法，用于加速采样N_f = 2格点施温格模型。受近期生成模型在格点场论中成功生成系综以克服临界慢化问题的启发，我们训练了一个U(1)等变的基于得分的生成模型，从边际施温格模型中采样规范链配置。通过计算模型似然，我们获得了与蒙特卡洛（MCMC）模拟结果高度吻合的无偏可观测量估计。此外，在临界参数附近，定性上显示其相比哈密顿蒙特卡洛（HMC）减少了拓扑冻结现象。
原文摘要 · Abstract (English)
We present a first study of a diffusion-based approach to accelerated sampling of the $N_f = 2$ lattice Schwinger model. Our work is inspired by recent and growing successes in developing such generative models for ensemble generation in LFT to overcome the well-known critical slowing down problem. We train a U(1)-equivariant score-based generative model to sample gauge link configurations from the marginal Schwinger model. By computing model likelihoods, we obtain unbiased estimates for observables that closely match those produced by MCMC simulations. We also demonstrate improvement over HMC as measured qualitatively by a reduction in topological freezing near critical parameters.
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