Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation

Abstract

Stochastic kriging (SK) methodology has been known as an effective metamodeling tool for approximating a mean response surface implied by a stochastic simulation. In this paper we provide some theoretical results on the predictive performance of SK, in light of which novel integrated mean squared error-based sequential design strategies are proposed to apply SK for mean response surface metamodeling with a fixed simulation budget. Through numerical examples of different features, we show that SK with the proposed strategies applied holds great promise for achieving high predictive accuracy by striking a good balance between exploration and exploitation.