Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression

In the context of uncertainty analysis, Polynomial chaos expansion (PCE) has been proven to be a powerful tool for developing meta-models in a wide range of applications, especially for sensitivity analysis. But the computational cost of classic PCE grows exponentially with the size of the input variables. An efficient approach to address this problem is to build a sparse PCE. In this paper, a full PCE meta-model is first developed based on...