  Uncertainty Quantification (UQ) is receiving more and more attention for
engineering applications in particular from robust optimization. Indeed,
running a computer experiment only provides a limited knowledge in terms of
uncertainty and variability of the input parameters. These experiments are
often computationally expensive and surrogate models can be constructed to
address this issue. The outcome of a UQ study is in this case directly
correlated to the surrogate's quality. Thus, attention must be devoted to the
Design of Experiments (DoE) to retrieve as much information as possible. This
work presents two new strategies for parameter space resampling to improve a
Gaussian Process (GP) surrogate model. These techniques indeed show an
improvement of the predictive quality of the model with high dimensional
analytical input functions. Finally, the methods are successfully applied to a
turbine blade Large Eddy Simulation application: the aerothermal flow around
the LS89 blade cascade.
