论文标题
采样QCD字段配置，具有量规等流量模型
Sampling QCD field configurations with gauge-equivariant flow models
论文作者
论文摘要
已经证明，基于归一化流量的机器学习方法已显示出在简单晶格野外理论中的量规场配置采样中，例如关键的减速和拓扑冻结等重要挑战。一个关键的问题是，这种成功是否会转化为QCD的研究。该程序介绍了该领域进步的状态更新。特别是，可以说明如何将最近开发的算法组件合并到四个维度的QCD基于流的采样算法。总结了这种方法在未来使用这种方法的前景和挑战。
Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gauge field configurations in simple lattice field theories. A critical question is whether this success will translate to studies of QCD. This Proceedings presents a status update on advances in this area. In particular, it is illustrated how recently developed algorithmic components may be combined to construct flow-based sampling algorithms for QCD in four dimensions. The prospects and challenges for future use of this approach in at-scale applications are summarized.
