General introduction to surrogate model-based approaches to UQ

Abstract

This chapter introduces two popular surrogate modeling methods which can be used to quantify uncertainties such as statistics of the aerodynamic coefficients from scattered data obtained by computational fluid dynamics (CFD) simulations. One is Kriging, which is able not only to interpolate predicted data but also to provide statistical information at unsampled locations in the parameter space based on Bayesian statistics. The other one is the radial basis function (RBF) method. The RBF method is also a powerful nonlinear interpolation method which exactly interpolates the samples, and its various radial basis function types support the interpolated values locally or globally when appropriately selected. Both methods can make use of gradient information, if available, to improve the model accuracy.