Ziyuan Wang’s research while affiliated with McGill University and other places

This paper investigates the application of tensor decomposition and the stochastic Galerkin method for the uncertainty quantification of complex systems characterized by high parameter dimensionality. By employing these methods, we construct surrogate models aimed at efficiently predicting system output uncertainty. The effectiveness of our approaches is demonstrated through a comparative analysis of accuracy and CPU cost with conventional Galerkin methods, using two transmission line circuit examples with up to 25 parameters.