For uncertainty quantification of complex models with high-dimensional, nonlinear, multi-component coupling like digital twins, traditional statistical sampling methods, such as random sampling and Latin hypercube sampling, require a large number of samples, which entails huge computational costs. Therefore, how to construct a small-size sample space has been a hot issue of interest for researchers. To this end, this paper proposes a sequential search-based Latin hypercube sampling scheme to generate efficient and accurate samples for uncertainty quantification. First, the sampling range of the samples is formed by carving the polymorphic uncertainty based on theoretical analysis. Then, the optimal Latin hypercube design is selected using the Latin hypercube sampling method combined with the “space filling” criterion. Finally, the sample selection function is established, and the next most informative sample is optimally selected to obtain the sequential test sample. Compared with the classical sampling method, the generated samples can retain more information on the basis of sparsity. A series of numerical experiments are conducted to demonstrate the superiority of the proposed sequential search-based Latin hypercube sampling scheme, which is a way to provide reliable uncertainty quantification results with small sample sizes.
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