A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification
Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and direct simulations are prohibitive. A pragmatic way to overcome this limitation is by calibrating low-fidelity computational predictions with experimental measurements. This, however, requires calibrating against \emph{sparse} measurements contaminated with \emph{uncertainty} in both the control inputs and the measured aerodynamic response. We develop a methodology to address this problem based on Gaussian process surrogates and the classical Kennedy-O'Hagan calibration. A surrogate model learned on abundant-but-cheap low-fidelity data is c...
| Authors | Geoffrey Davis, Ashwin Renganathan |
|---|---|
| Primary category | stat.ML |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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