Conformalized Polynomial Chaos Expansion for Uncertainty-aware Surrogate Modeling

The paper presents a technique that augments data‑driven polynomial chaos expansion surrogate models with uncertainty quantification through jackknife‑based conformal prediction. By leveraging the linearity of the regression model, leave‑one‑out residuals and predictions are computed analytically, avoiding repeated retraining and eliminating the need for a hold‑out dataset. The method is validated on benchmark models and applied to electrical engineering design cases, demonstrating accurate predictive intervals even with small, low‑accuracy training sets, while showing that more training data improves coverage and interval tightness.

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