Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators
Bridging the gap between neural operators and physical consistency for reliable scientific simulation.
Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (PDEs). However, providing rigorous uncertainty estimates remains an open challenge. We propose Physics-Informed Conformal Predictio...
ArXiv CS.LG
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Integrating physics constraints into conformal prediction bridges the gap between deep learning speed and the rigorous reliability required for scientific discovery. This method ensures that AI-generated scientific simulations respect fundamental physical laws while providing statistically valid uncertainty bounds.
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