Efficiency of the Perturbative Stochastic Galerkin Method for Multiple Differential PCB Lines
Xinglong Wu, Flavia Grassi, Paolo Manfredi, Dries Vande Ginste
Abstract
Xinglong Wu, Flavia Grassi, Paolo Manfredi, Dries Vande Ginste
Abstract
This paper investigates the efficiency of a perturbative approach for the statistical assessment of differential transmission lines affected by random parameters. Within the polynomial chaos framework, the novel technique reformulates the so-called stochastic Galerkin method in a decoupled and iterative fashion. Instead of solving the classical, augmented and fully coupled transmission line equations, the new approach iteratively solves multiple uncoupled line equations with nominal per-unit-length parameters and suitable equivalent distributed sources accounting for their variability. The methodology is applied to a system of up to four PCB differential pairs. A computational advantage is observed against the classical stochastic Galerkin method for large problems in terms of number of random parameters and/or conductors.
OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This paper investigates the efficiency of a perturbative approach for the statistical assessment of differential transmission lines affected by random parameters. Within the polynomial chaos framework, the novel technique reformulates the so-called stochastic Galerkin method in a decoupled and iterative fashion. Instead of solving the classical, augmented and fully coupled transmission line equations, the new approach iteratively solves multiple uncoupled line equations with nominal per-unit-length parameters and suitable equivalent distributed sources accounting for their variability. The methodology is applied to a system of up to four PCB differential pairs. A computational advantage is observed against the classical stochastic Galerkin method for large problems in terms of number of random parameters and/or conductors.
Key concepts: Polynomial chaos, Galerkin method, Applied mathematics, Line (geometry), Mathematics, Transmission line, Stochastic differential equation, Differential equation