Algorithms for Improving the Designs of Building Universal Kriging Models

Kriging models (KMs) have been used popularly in the analysis of computer experiments (CEs). Fast-running alternative models for computationally demanding computer codes (CC) are created through Kriging models. To predict the CC response at untested observations using the values at observed points, the design points should be carefully selected. Latin hypercube design (LHD) is a stratified design that has become popular for building KMs. However, in some cases, additional points may need to be added to the LHD to improve its performance. Several algorithms have been proposed for this purpose. This work aims to present and evaluate the performance of several algorithms to improve LHD performance. Therefore, several KMs are constructed based on various algorithms for generating the design points, and their performance is compared. We propose some measures for comparing the performance of KMs applied to real CCs.