Efficient sparse polynomial chaos expansion methodology for computationally expensive deterministic models

International audienceThe sparse polynomial chaos expansion (SPCE) methodology is an efficient approach that deals with uncertainties propagation in case of high-dimensional problems (i.e. when a large number of random variables is involved). This methodology significantly reduces the computational cost with respect to the classical full polynomial chaos expansion (PCE) methodology. Notice however that when dealing with computationally-expensive deterministic models, the time cost remains important even with the use of the SPCE. In this paper, an efficient combined use of the SPCE methodology and the global sensitivity analysis (GSA) is proposed to solve such a problem. The proposed methodology is validated using a relatively non-expensive deterministic model