Kriging hyperparameter tuning strategies

Abstract

Response surfaces have been extensively used as a method of building effective surrogate models of high-fidelitycomputational simulations. Of the numerous types of response surface models, kriging is perhaps one of the mosteffective, due to its ability to model complicated responses through interpolation or regression of known data whileproviding an estimate of the error in its prediction. There is, however, little information indicating the extent to whichthe hyperparameters of a kriging model need to be tuned for the resulting surrogate model to be effective. Thefollowing paper addresses this issue by investigating how often and how well it is necessary to tune thehyperparameters of a kriging model as it is updated during an optimization process. To this end, an optimizationbenchmarking procedure is introduced and used to assess the performance of five different tuning strategies over arange of problem sizes. The results of this benchmark demonstrate the performance gains that can be associated withreducing the complexity of the hyperparameter tuning process for complicated design problems. The strategy oftuning hyperparameters only once after the initial design of experiments is shown to perform poorly