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Found 3 papers, 2 papers with code
Operator learning without the adjoint
There is a mystery at the heart of operator learning: how can one recover a non-self-adjoint operator from data without probing the adjoint?
Elliptic PDE learning is provably data-efficient
PDE learning is an emerging field that combines physics and machine learning to recover unknown physical systems from experimental data.
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks
Here, we prove that operator NNs of bounded width and arbitrary depth are universal approximators for continuous nonlinear operators.
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