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Derivative-Informed Training of Neural Operators On-the-Fly via Sketched Tangent Consistency
Derivative-informed training improves neural operators by directly supervising their input-output sensitivities, which is crucial when neural operators are used as differentiable surrogates for inverse problems, PDE-constrained optimization, design, and control. However, existing methods rely on off
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