Simplified Cross-Correlation Estimation For Multi-Fidelity Surrogate Cokriging Models

Abstract

Multi-fidelity surrogate modeling refers to the enhanced prediction of the output of a complex system by incorporating auxiliary fast-to-obtain data of lower fidelity; one such technique being Cokriging. In order to construct Cokriging predictors it is mandatory to estimate certain co- and cross-variances based on sampled data. In this paper, a simple method to estimate these quantities is introduced, reducing the number of total model tuning parameters to that of standard one-fidelity Kriging prediction plus one. Prediction behavior is demonstrated on academic examples as well as on an aerodynamic engineering problem. Results are compared with those obtained by applying the predictor model suggested by Kennedy and O’Hagan in 2000.