A Nonparametric Surrogate Model for Stochastic Crosstalk Analysis Including Confidence Bounds

This paper introduces a probabilistic nonparametric surrogate model based on Gaussian process regression to perform uncertainty quantification tasks with the inclusion of confidence bounds on the predicted statistics. The performance of the proposed method is compared against two state-of-the-art techniques, namely the parametric sparse polynomial chaos expansion and the nonparametric least-square support vector machine regression.