Using a conformal map to a reference annulus makes a latent neural operator for the 2D Laplace equation about ten times more accurate and far more data-efficient than using LDDMM or optimal transport maps.
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Diffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations
Using a conformal map to a reference annulus makes a latent neural operator for the 2D Laplace equation about ten times more accurate and far more data-efficient than using LDDMM or optimal transport maps.