<ul><li>A new approach called PreCorrector has been developed to enhance classical preconditioners for linear systems using neural networks.</li><li>PreCorrector outperforms classical preconditioners like IC(0) and ICt(1) in constructing better preconditioners for complex linear systems.</li><li>The neural design of PreCorrector improves efficiency and memory trade-off by achieving speed-ups compared to traditional preconditioners.</li><li>The PreCorrector approach showcases good generalization across different grids and datasets, maintaining quality during transfers for inference.</li></ul>

PreCorrector Proves Its Worth: Classical Preconditioners Meet Their Neural Match

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