Supervising a neural operator's predicted Jacobian du/dp during training, called SC-FNO, sharply improves parameter inversion, sensitivity accuracy, and robustness to parameter shift compared to standard FNO and FNO with physics-informed regularization.
A surrogate-model-based method for constrained optimization
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
Supervising a neural operator's predicted Jacobian du/dp during training, called SC-FNO, sharply improves parameter inversion, sensitivity accuracy, and robustness to parameter shift compared to standard FNO and FNO with physics-informed regularization.