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Learning in the Transverse Subspace: A Minimal Representation for Divergence-Free Operator Learning
Divergence-free vector fields are fundamental state variables in incompressible flows and many PDE systems. Redundant parameterizations, including Neural Conservation Law (NCL) potentials, map multiple auxiliary representations to the same physical field. Our experiments show that this redundancy ca
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