Fast maximum likelihood estimate of the Kriging correlation range in the frequency domain

We apply Ordinary Kriging to predict 75,000 terrain survey data from a randomly sampled subset of < 2500 observations. Since such a Kriging prediction requires a considerable amount of CPU time, we aim to reduce its computational cost. In a conventional approach, the cost of the Kriging analysis would be dominated by the optimization routine...

Reducing uncertainties in a wind-tunnel experiment using Bayesian updating

We perform a fully stochastic analysis of an experiment in aerodynamics. Given estimated uncertainties on the principle input parameters of the experiment, including uncertainties on the shape of the model, we apply uncertainty propagation methods to a suitable CFD model of the experimental setup. Thereby we predict the stochastic response of...