Use a sequential gradient-enhanced-Kriging optimal experimental design method to build high-precision approximate model for complex simulation problem

Abstract

The surrogate model based on Kriging has been widely used to approximate simulation problems of expensive computing. Although the accuracy of the gradient enhanced Kriging (GEK) is often higher than that of ordinary Kriging, designers cannot avoid more time consuming during gradient calculation of GEK. To this end, a sequential gradient-enhanced-Kriging optimal experimental design method with the Gaussian correlation function (GCF) is investigated to approximate complex black-box simulation problems by introducing gradient information of Kriging parameters. Due to the differentiable GCF, the gradient information can be simply evaluated. This characteristic make the proposed method effectively improve the modeling accuracy and efficiency of GEK. As expected, the test results from benchmark functions and the cycloid gear pump simulation show the feasibility, stability and applicability of the proposed method.