A kernel operator learning framework constructs property-preserving bases so that predicted incompressible velocity fields satisfy divergence-free and periodicity conditions exactly, delivering up to six orders lower error and five orders faster training than neural operators.
MF Alam, David S Thompson, and D Keith Walters
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Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
A kernel operator learning framework constructs property-preserving bases so that predicted incompressible velocity fields satisfy divergence-free and periodicity conditions exactly, delivering up to six orders lower error and five orders faster training than neural operators.