Constructing meta-models for computer experiments

We used three test functions to compare all combinations of five experimental design classes with either second-order response surface (RS) or kriging modeling methods. The findings included the following: 1) conclusions about which method performed best, even for a single case study, greatly depended on the specific experimental designs used to represent each class of designs; 2) unavoidable bias errors constituted the largest source of prediction errors when regression modeling was used with designs generated to address bias errors; and 3) estimation errors, which could be attributed to the use of the likelihood estimation objective, dominated prediction errors in kriging modeling. We tentatively conclude that, for cases in which the number of runs is comparable to the number of terms in a quadratic polynomial model, similar prediction errors can be expected from both kriging and regression modeling procedures as long as regression is used in combination with experimental designs generated to address bias errors.