论文标题
部分可观测时空混沌系统的无模型预测
Aspects of scaling and scalability for flow-based sampling of lattice QCD
论文作者
论文摘要
机器学习的归一化流与晶格场理论中采样的最新应用表明，这种方法可能能够减轻关键的减速和拓扑结冰。但是，这些示例一直处于玩具模型的规模，还有待确定是否可以应用于最新的晶格量子染色体动力学计算。传统上，使用简单的成本缩放定律来评估大规模晶格场理论的采样算法的生存能力，但是正如我们在这项工作中讨论的那样，它们的实用性限制了基于流的方法。我们得出的结论是，基于流的采样方法可以更好地认为是具有不同缩放特性的广泛算法家族，并且必须通过实验评估可伸缩性。
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations. Assessing the viability of sampling algorithms for lattice field theory at scale has traditionally been accomplished using simple cost scaling laws, but as we discuss in this work, their utility is limited for flow-based approaches. We conclude that flow-based approaches to sampling are better thought of as a broad family of algorithms with different scaling properties, and that scalability must be assessed experimentally.
