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

Abstract

Abstract. This article presents a new adaptive sequential sampling strategy for surrogate models based on the maximin distance criterion space filling of the system response quantity (SRQ). The strategy enables sequential adaptive sampling of an experimental design while attempting to cover SRQ and input 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 input spaces, with a clear sequential screening mechanism that first ranks candidates by predicted SRQ space dispersion and then optimizes input space space filling. The proposed criterion for the sample selection balances both filling predicted SRQ space of the surrogate model and the input space in a computationally efficient heuristic framework. This article adopts polynomial chaos Kriging as the surrogate model. The superiority of the SRQ-Mm method over pure SRQ space filling and input space filling is discussed in detail through theoretical analysis and numerical simulations. The proposed strategy is also compared with the widely used EIGF and MiVor adaptive approaches. The numerical results confirm its superiority over existing adaptive sampling approaches in terms of surrogate model accuracy, computational efficiency, and robustness.