Learning in the Transverse Subspace: A Minimal Representation for Divergence-Free Operator Learning
This work introduces a minimal representation that encodes a real divergence-free vector field on a \(D\)-dimensional domain as a real \((D-1)\)-component field on the same domain, and uses a Householder orthogonal transformation to construct the reduced coordinates directly.
Yi-Fei Sun
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