AINeutralarXiv – CS AI · 10h ago6/10
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CATO: Charted Attention for Neural PDE Operators
Researchers introduce CATO (Charted Axial Transformer Operator), a neural operator architecture that solves partial differential equations (PDEs) on complex geometries more efficiently than existing methods. By learning geometry-adaptive coordinate transformations and incorporating derivative-aware physics supervision, CATO achieves 26.76% performance improvement over competing approaches while reducing parameters by 82%.