To achieve fast convergence rate and modelling stability, the proposed strategy takes into account both input information and target model feature by combining compressive sampling and Bayesian experimental design. In this paper, a hybrid sequential sampling strategy is proposed to collect samples with high quality and in relatively small quantities for training PCE model. The sample quality is a crucial issue that affects the precision of sparse PCE model. Sparse representation of Polynomial Chaos Expansion (PCE) has been widely used in the field of Uncertainty Quantification (UQ) due to its simple model structure and low computational cost.
