Gauge-equivariant neural networks as preconditioners in lattice QCD

02/10/2023

∙

by   Christoph Lehner, et al.

∙

0

∙

share

We demonstrate that a state-of-the art multi-grid preconditioner can be learned efficiently by gauge-equivariant neural networks. We show that the models require minimal re-training on different gauge configurations of the same gauge ensemble and to a large extent remain efficient under modest modifications of ensemble parameters. We also demonstrate that important paradigms such as communication avoidance are straightforward to implement in this framework.