A supervised neural operator maps pixelated images of 2D domains to Dirichlet eigenvalues and eigenfunctions, achieving about 1% relative eigenvalue error on held-out random shapes.
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Operator Inference for Elliptic Eigenvalue Problems
A supervised neural operator maps pixelated images of 2D domains to Dirichlet eigenvalues and eigenfunctions, achieving about 1% relative eigenvalue error on held-out random shapes.