Response-Adaptive Space-Filling Sampling Strategy for Efficient Surrogate modeling

Abstract This paper presents a new adaptive sequential sampling strategy for surrogate models based on maximin-distance-criterion space-filling of system response quantity (SRQ). The technique enables one-by-one extension of an experimental design while trying to cover SRQ and design spaces at each stage of the adaptive sequential surrogate model construction process. The proposed adaptive sampling strategy selects a new sample point from a pool of candidate design points based on dual maximin distance designs in the SRQ and design spaces. The proposed criterion for the sample selection balances both filling prediction SRQ space of the surrogate model and the design domain. This paper chooses polynomial chaos Kriging as surrogate model. The proposed strategy is compared with the widely used EIGF and MiVor adaptive approaches. The numerical results confirm its superiority over ElGF and MiVor adaptive sampling approaches in terms of surrogate model accuracy and effectiveness.