Performance estimate and simultaneous application of multiple surrogates

A typical approach in surrogate-based modeling is to assess the performance of alternative surrogate models and select the model that performs the best. In this paper, we extend the utility of an ensemble of surrogates to: i) identify regions of high uncertainties at locations where predictions of surrogates widely differ, and ii) provide a more robust approximation approach. We explore the possibility of using the best surrogate or a weighted average surrogate model instead of individual surrogate models. The weights associated with each surrogate model are determined based on the errors in surrogates. We demonstrate the advantages of an ensemble of surrogates using analytical problems and an engineering problem of radial turbine design for space launch vehicle. We show that for a single problem the choice of the surrogate can be substantially influenced by the design of experiments.