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New method enhances uncertainty quantification in surrogate models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Conformalized Polynomial Chaos Expansion (CPCE) to improve the uncertainty quantification of data-driven surrogate models. This technique integrates jackknife-based conformal prediction into polynomial chaos expansions, generating predictive intervals that accurately reflect the model's uncertainty. CPCE allows the full dataset to be used for training without needing a separate calibration set, and it leverages analytical expressions for efficiency. The method has been validated on benchmark models and applied to electrical engineering design, demonstrating its effectiveness even with small datasets and low-accuracy models. AI

IMPACT This method could improve the reliability of AI-driven simulations and predictions in engineering and scientific applications.

RANK_REASON The cluster contains an academic paper detailing a new methodology. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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New method enhances uncertainty quantification in surrogate models

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The cluster contains an academic paper detailing a new methodology. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Dimitrios Loukrezis, Dimitris G. Giovanis ·

    Conformalized Polynomial Chaos Expansion for Uncertainty-aware Surrogate Modeling

    arXiv:2510.22375v4 Announce Type: replace-cross Abstract: This work introduces a method to equip data-driven polynomial chaos expansion surrogate models with intervals that quantify the predictive uncertainty of the surrogate. To that end, jackknife-based conformal prediction is …

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