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PGD-NO is a neural operator that precomputes geometry decomposition to achieve linear memory scalability, enabling high-fidelity physics simulations on meshes exceeding 10 million nodes and overcoming the single-node memory bottleneck.
Introduces UFO, a cross-domain neural operator framework that adaptively learns operators across different representational domains, enabling discretization-decoupled predictions robust to distribution shifts.