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.
Learning about physical parameters: The importance of model discrepancy.Inverse Problems, 30(11):114007, 2014
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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.