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Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators
Not provided in the abstract
uncertainty quantificationneural operatorsPDEsconformal prediction
2609.11935
Builder Relevance
1h ago80%
Abstract
The paper presents a framework for providing rigorous uncertainty estimates in neural operators for PDEs.
Reality Card
Core Claim
The proposed Physics-Informed Conformal Prediction (PI-CP) framework produces distribution-free prediction intervals with provable coverage guarantees and spatial adaptivity based on PDE residuals.
Method / Result
Achieved up to 63x error reduction in approximation for PDEs with Dirichlet boundary conditions using coordinate channels.
Limitations
The paper does not specify limitations or reproducibility concerns.
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