Number of DoE Required for Estimating Different Quality Surrogate

Abstract

Surrogate models are commonly used in place of computationally expensive simulations in engineering design and optimization, and the predictive performance of surrogate models is usually influenced by the quality of design of experiments (DoE). One way to eliminate the effect of the randomness of DoE is to average multiple prediction accuracies over multiple DoEs. However, how many DoEs are needed to obtain stable prediction results for problems with different dimensionalities remains a challenging issue. Mathematical test functions have been employed in a large body of literatures to identify the predictive performance of surrogate models. In this work, 30 test functions ranging from 1 dimension to 16 dimensions are selected to investigate the relationship between the number of DoEs needed for a stable prediction accuracy and the number of sample points. A convergence condition is used to determine whether a reliable model accuracy has been obtained. In this paper, the number of DoEs required for estimating the model accuracy is provided as a suggestion for those who develop surrogate models and select test functions to validate the performance of models.