This paper demonstrates the application of correlated Gaussian process based
approximations to optimization where multiple levels of analysis are available, using an
extension to the geostatistical method of co-kriging. An exchange algorithm is used to
choose which points of the search space to sample within each level of analysis. The
derivation of the co-kriging equations is presented in an intuitive manner, along with a new
variance estimator to account for varying degrees of computational ‘noise’ in the multiple
levels of analysis. A multi-fidelity wing optimization is used to demonstrate the
methodology
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