Gauge-equivariant neural networks as preconditioners in lattice QCD
Christoph Lehner, Tilo Wettig
2/10/2023
hep-latcs.LGmath.NA
No confident code implementation found for this paper.
Abstract
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.
