volume | PIER Journals

Abstract

Polynomial Chaos Expansion (PCE) is widely utilized in uncertainty quantification (UQ) for electromagnetic compatibility (EMC) due to its robust global predictive capabilities. However, its computational overhead increases exponentially with stochastic dimensionality, leading to the notorious curse of dimensionality. To address this bottleneck, this paper proposes a generalized preprocessing dimensionality reduction framework designed to enhance the performance of PCE. By decoupling dimensional screening from predictive modeling, the proposed framework first employs low-cost estimators to identify significant random variables. Subsequently, an improved PCE model is constructed within the reduced feature space. Given the prohibitively high computational cost of acquiring EMC simulation samples, this study instantiates a screening module within the framework that integrates Least Squares Support Vector Regression (LSSVR) with Sobol indices. Finally, the proposed framework-based method is applied to a cable crosstalk case study to validate its effectiveness and engineering applicability.

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Dr. Yitong Lu

Prof. Zhengyu Xue

Dr. Shenghang Huo