Review of Dace-Kriging Metamodel

This paper presents a conceptual review of the kriging metamodel that is introduced for the design and analysis of computer experiments (DACE). Kriging is a statistical interpolation method to build an approximation model from a set of evaluations of the function at a finite set of points. The method originally developed for geostatistics, and it is now widely used in the domains of spatial data analysis and computer experiments analysis. The main difference between these domains the dimensionality of the problems. Geostatistics and spatial data are mainly deal with the coordinates. Computer experiments, simulation outputs and other engineering problems have multidimensional input variables. With this study, it is aimed to examine the limitations of the prediction performance of the DACE-kriging metamodel. The result of the study shows that the regression part of the DACE-kriging metamodel is the most important part to develop an approximation, and if there is a spatial relationship of the residuals, kriging part will also contribute to the improvement of the prediction performance. Otherwise, kriging will have no contribution to the DACE-kriging metamodel, and even worsen the prediction performance. If the regression part perfectly fit to the observations, the residual will have poor spatial relationship and the kriging part will be meaningless anymore.