Support Vector Regression-Based Active Subspace (SVR-AS) Modeling of High-Speed Links for Fast and Accurate Sensitivity Analysis

Hanzhi Ma, Er Ping Li, Andreas C. Cangellaris, Xu Chen

Research output: Contribution to journalArticle

Abstract

A methodology based on the joint usage of support vector regression and active subspace is introduced in this paper for accelerated sensitivity analysis of high-speed links through parameter space dimensionality reduction. The proposed methodology uses the gradient directly obtained by support vector regression with Gaussian kernel to generate an active subspace with its application to the high-speed link model. Active subspace generated by this method is defined by the directions that are most influential on the desirable output measure. The resulting reduced-dimensional model is shown to perform well in sensitivity analysis of high-speed links including IBIS-AMI equalization, and is computationally more efficient than Sobol's method.

Original languageEnglish (US)
Article number9068208
Pages (from-to)74339-74348
Number of pages10
JournalIEEE Access
Volume8
DOIs
StatePublished - Jan 1 2020

Keywords

  • High-speed link
  • active subspace
  • dimensionality reduction
  • eye diagram
  • sensitivity analysis
  • support vector regression
  • surrogate modeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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