Gauge-equivariant neural networks as preconditioners in lattice QCD
- April 6 (Thu) at 13:30 - 15:00, 2023 (JST)
- Tilo Wettig (Professor, Universität Regensburg, Germany)
- Tetsuo Hatsuda
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.
- Christoph Lehner and Tilo Wettig, Gauge-equivariant neural networks as preconditioners in lattice QCD, (2023), arXiv: 2302.05419
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