Authors’ Online Seminar – IEEE Journal on Multiscale and Multiphysics Computational Techniques
This seminar is based on the paper “A Hybrid EM/QM Framework Based on the ADHIE-FDTD Method for the Modeling of Nanowires“, IEEE J-MMCT, vol. 7, 2022, pp. 236-251, coauthored by Pieter Decleer and Dries Vande Ginste. The paper won the Best Paper Award for papers published in IEEE J-MMCT in years 2021-2022. In the seminar […]
A Two-Step Approach for the Analysis of Bulk Current Injection Setups Involving Multiwire Bundles
In this work, a two-step procedure to predict maximum(worst-case scenario) and minimum (best-case scenario) noise levels induced by bulk current injection (BCI) at the terminal sections of awiring harness is presented.To this end, common mode (CM) and differential mode (DM) quantities are introduced by a suitable modal transformation, and equivalent modal circuits are derived, where […]
Uncertainty Quantification of Electromagnetic Devices, Circuits, and Systems
Profs. Paolo Manfredi and Dries Vande Ginste authored Chapter 2 (Polynomial Chaos Based Uncertainty Quantification in Electrical Engineering: Theory) and Chapter 3 (Polynomial Chaos Based Uncertainty Quantification in Electrical Engineering: Applications) of the book Uncertainty Quantification of Electromagnetic Devices, Circuits, and Systems, edited by Prof. Sourajeet Roy and published by the Institution of Engineering and […]
A perturbative stochastic Galerkin method for the uncertainty quantification of linear circuits
This paper presents an iterative and decoupled perturbative stochastic Galerkin (SG) method for the variability analysis of stochastic linear circuits with a large number of uncertain parameters. State-of-the-art implementations of polynomial chaos expansion and SG projection produce a large deterministic circuit that is fully coupled, thus becoming cumbersome to implement and inefficient to solve when […]
Scattering parameters characterization of periodically nonuniform transmission lines with a perturbative technique
In this article, a novel procedure for the frequency-domain solution of nonuniform transmission lines (NUTLs) is presented. The procedure is based on a recently proposed perturbative technique, which is proven to be computationally more efficient than standard solution approaches, which are based on line subdivision into uniform cascaded sections. With respect to the original perturbation […]
Machine-learning-based hybrid random-fuzzy uncertainty quantification for EMC and SI assessment
Modeling the effects of uncertainty is of crucial importance in the signal integrity and Electromagnetic Compatibility assessment of electronic products. In this article, a novel machine-learning-based approach for uncertainty quantification problems involving both random and epistemic variables is presented. The proposed methodology leverages evidence theory to represent probabilistic and epistemic uncertainties in a common framework. […]