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Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
We present a novel property-preserving kernel-based operator learning method for incompressible flows governed by the incompressible Navier--Stokes equations. Traditional numerical solvers incur significant computational costs to respect incompressibility. Operator learning offers efficient surrogat
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