Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (FNO) can efficiently approximate solutions, they struggle with inverse problems, sensitivity estimation (du/dp), and concept drift. We address these limitations by introducing a sensitivity-based regularization strategy, called Sensitivity-Constrained Fourier Neural Operators (SC-FNO). SC-FNO achieves high accuracy in predicting solution paths and consistently outperforms standard FNO and FNO with physics-informed regularization. It improves performance in parameter inversion tasks, scales to high-dimensional parameter spaces (tested with up to 82 parameters), and reduces both data and training requirements. These gains are achieved with a modest increase in training time (30% to 130% per epoch) and generalize across various types of differential equations and neural operators. Code and selected experiments are available at:this https URL
View on arXiv@article{behroozi2025_2505.08740,
title={ Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations },
author={ Abdolmehdi Behroozi and Chaopeng Shen and and Daniel Kifer },
journal={arXiv preprint arXiv:2505.08740},
year={ 2025 }
}