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Gauge-equivariant pooling layers for preconditioners in lattice QCD
We demonstrate that gauge-equivariant pooling and unpooling layers can perform as well as traditional restriction and prolongation layers in multigrid preconditioner models for lattice QCD.
Gauge-equivariant neural networks as preconditioners in lattice QCD
We demonstrate that a state-of-the art multi-grid preconditioner can be learned efficiently by gauge-equivariant neural networks.
Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning
This displays how a factor of ten speedup in strongly scaled distributed machine learning could be achieved when synchronous stochastic gradient descent is massively parallelised with a fixed mini-batch size.
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