High-Dimensional Variability Analysis via Parameters Space Partitioning

Abstract

A stochastic collocation-based method is proposed for variability analysis in the presence of relatively large number of stochastic parameters. The method is based on the node tearing concept where the original space of stochastic parameters is decomposed into a combination of lower dimension subspaces. Pertinent numerical results are presented to validate the efficiency and accuracy of the proposed technique.