  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.
