Practical Implementation of Stochastic Parameterized Model Order Reduction via Hermite Polynomial Chaos

This paper describes the stochastic model order reduction algorithm via stochastic Hermite polynomials from the practical implementation perspective. Comparing with existing work on stochastic interconnect analysis and parameterized model order reduction, we generalized the input variation representation using polynomial chaos (PC) to allow for accurate modeling of non-Gaussian input variations. We also explore the implicit system representation using sub-matrices and improved the efficiency for solving the linear equations utilizing block matrix structure of the augmented system. Experiments show that our algorithm matches with Monte Carlo methods very well while keeping the algorithm effective. And the PC representation of non-Gaussian variables gains more accuracy than Taylor representation used in previous work (Wang et al., 2004).