Researchers have developed a new Bayesian latent Gaussian process framework to improve the accuracy of aerodynamic uncertainty quantification. This methodology calibrates low-fidelity computational models using sparse experimental measurements, which are themselves subject to uncertainty. The framework effectively marginalizes over input uncertainty and matches the mean and variance of output uncertainty, leading to highly accurate predictions of aerodynamic coefficients, even in extrapolative scenarios. Validation showed the model's predictions falling within true uncertainty intervals with high fidelity. AI
IMPACT This framework could improve the reliability of simulations in fields like aerospace engineering by better accounting for uncertainty.
RANK_REASON The cluster contains an academic paper detailing a new methodology.
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