Stochastic Collocation with Non-Gaussian Correlated Parameters via a New Quadrature Rule
For engineers performing uncertainty quantification in electronic/photonic circuits, this method dramatically accelerates simulation with correlated non-Gaussian inputs.
This paper extends stochastic collocation to correlated non-Gaussian parameters using a new quadrature rule, achieving 3000x speedup over Monte Carlo on CMOS and optical ring resonator benchmarks.
This paper generalizes stochastic collocation methods to handle correlated non-Gaussian random parameters. The key challenge is to perform a multivariate numerical integration in a correlated parameter space when computing the coefficient of each basis function via a projection step. We propose an optimization model and a block coordinate descent solver to compute the required quadrature samples. Our method is verified with a CMOS ring oscillator and an optical ring resonator, showing 3000x speedup over Monte Carlo.