Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

This paper presents a novel adaptive sequential sampling method for building Polynomial Chaos Expansion surrogate models. The technique enables one-by-one extension of an experimental design while trying to obtain an optimal sample at each stage of the adaptive sequential surrogate model construction process. The proposed sequential sampling strategy selects from a pool of candidate points by trying to cover the design domain proportionally to their local variance contribution. The proposed criterion for the sample selection balances both exploitation of the surrogate model and exploration of the design domain. The adaptive sequential sampling technique can be used in tandem with any user-defined sampling method, and here was coupled with commonly used Latin Hypercube Sampling and advanced Coherence D-optimal sampling in order to present its general performance. The obtained numerical results confirm its superiority over standard non-sequential approaches in terms of surrogate model accuracy and estimation of the output variance. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a novel adaptive sequential sampling method for constructing Polynomial Chaos Expansion surrogate models. The technique aims to obtain an optimal sample at each stage by extending the experimental design one by one. The strategy selects candidate points proportionally to their local variance contribution, balancing the exploitation of the surrogate model and exploration of the design domain.