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New framework enhances uncertainty quantification in reduced-order models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for quantifying uncertainty in non-intrusive reduced-order models (NIROMs). This method combines stochastic representation of reduced bases with conformal risk control techniques to provide reliable prediction sets. The approach separates basis-induced from regression-induced uncertainty without retraining Gaussian processes, offering a more accurate assessment of model reliability, particularly in extrapolation scenarios. AI

IMPACT Enhances reliability of computational models, potentially improving AI-driven design and simulation accuracy.

RANK_REASON The cluster contains an academic paper detailing a new methodology for uncertainty quantification in computational modeling. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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New framework enhances uncertainty quantification in reduced-order models

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The cluster contains an academic paper detailing a new methodology for uncertainty quantification in computational modeling. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Edgar Jaber (CB, ENS Paris Saclay), R\'emy Vallot (CB, Michelin), Thibault Dairay (CB, Michelin), Mathilde Mougeot (CB, ENSIIE, ENS Paris Saclay) ·

    Conformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models

    arXiv:2608.03360v1 Announce Type: new Abstract: Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer design of experiments while significantly reducing computational costs. However, asses…

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