标题： 机器学习对晶格相关数据的映射 显示英文标题

标题： Machine learning mapping of lattice correlated data

摘要： 我们讨论一种机器学习（ML）回归模型，以降低格点QCD计算中不连接图的计算成本。 该方法在不同夸克质量与流时间下计算的费米子环结果之间建立映射。 该ML映射仅使用完整数据集的一小部分进行训练，利用平移不变性，在整个集合上计算的结果具有可比的不确定性，从而实现了显著的计算收益。 显示英文摘要

摘要： We discuss a machine learning (ML) regression model to reduce the computational cost of disconnected diagrams in lattice QCD calculations. This method creates a mapping between the results of fermionic loops computed at different quark masses and flow times. The ML mapping, trained with just a small fraction of the complete data set, makes use of translational invariance and provides consistent result with comparable uncertainties over the calculation done over the whole ensemble, resulting in a significant computational gain.