Zusammenfassung

We demonstrate that a state-of-the-art multigrid preconditioner can be learned efficiently by gaugeequivariant neural networks. We show that the models require minimal retraining 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.