A Bayesian latent GP calibration framework for aerodynamic surrogates marginalizes input uncertainty and matches output uncertainty statistics, achieving 94.2-95.8% coverage of true 95% intervals.
Gramacy.Surrogates: Gaussian Process Modeling, Design, and Optimiza- tion for the Applied Sciences
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autonugget uses Richardson extrapolation across multiple regularized linear solves to produce stable, autodiff-compatible solutions for ill-conditioned systems without selecting a single nugget.
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A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification
A Bayesian latent GP calibration framework for aerodynamic surrogates marginalizes input uncertainty and matches output uncertainty statistics, achieving 94.2-95.8% coverage of true 95% intervals.
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Extrapolating from Regularised Solutions for Solving Ill-Conditioned Linear Systems in Machine Learning
autonugget uses Richardson extrapolation across multiple regularized linear solves to produce stable, autodiff-compatible solutions for ill-conditioned systems without selecting a single nugget.