发表机构
Institute of Physics, NAWI Graz, University of Graz（格拉茨大学NAWI格拉茨物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出DM-SMC方法，结合扩散模型与序贯蒙特卡洛，在近似模型和有限步长下实现精确采样，并在二维$Z_2$标量场论相变附近验证。
AI 中文摘要
本文研究了一种名为DM-SMC（扩散模型-序贯蒙特卡洛）的新方案，该方案利用在系综样本上训练的扩散模型，对由作用量定义的系综进行采样。SMC框架允许在扩散模型近似以及随机过程数值求解中有限步长的情况下实现精确采样。本文还研究了改进的更新策略。文中给出了二维$Z_2$对称标量场理论在其二级相变附近的结果。
英文摘要
A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using diffusion models trained on samples from the ensemble. The SMC setup allows for accurate sampling in spite of an approximate diffusion model and the finite stepsize used in the numerical solution of the stochastic process. Improved update strategies are also investigated. Results are presented for a $Z_2$ symmetric scalar field theory in 2 dimensions near its 2nd order phase transition.
Comments12 pages, 6 figures
