A Compromise Method for the Design of Parametric Polynomial Surrogate Models
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This study presents a compromise approach to augmentation of response surface (RS) designs to achieve the desired level of accuracy. RS are frequently used as surrogate models in multidisciplinary design optimization of complex mechanical systems. Augmentation is necessitated by the high computational expense typically associated with each function evaluation. As a result previous results from lower fidelity models are incorporated into the higher fidelity RS designs. The compromise approach yields higher quality parametric polynomial response surface approximations than traditional augmentation. Based on the D-optimality criterion as a measure of RS design quality, the method simultaneously considers several polynomial models during the RS design, resulting in good quality designs for all models under consideration, as opposed to good quality designs only for lower order models as in the case of traditional augmentation. Several numerical and an engineering example are presented to illustrate the efficacy of the approach.