We demonstrate that gauge-equivariant pooling and unpooling layers can perform as well as traditional restriction and prolongation layers in multigrid preconditioner models for lattice QCD. These layers introduce a gauge degree of freedom on the coarse grid, allowing for the use of explicitly gauge-equivariant layers on the coarse grid. We investigate the construction of coarse-grid gauge fields and study their efficiency in the preconditioner model. We show that a combined multigrid neural network using a Galerkin construction for the coarse-grid gauge field eliminates critical slowing down.
翻译:我们证明,规范等变池化和反池化层在多重网格预条件器模型中可以像传统的限制和延拓层一样有效。这些层在粗网格上引入了一个规范自由度,允许在粗网格上使用显式地规范等变层。我们研究了粗网格规范场的构造,并研究了它们在预条件器模型中的效率。我们展示了使用Galerkin方法构建粗网格规范场的组合多重网格神经网络可以消除关键减速。