New framework enhances uncertainty quantification in reduced-order models

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]

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• arXiv
• Edgar Jaber
• Gaussian Processes
• Stiefel manifold

• paper
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