Batch Training of Gaussian Process for Up-sampling Problems in S-Parameter Predictions

Yiliang Guo, Xingchen Li, Yifan Wang, Rahul Kumar, Madhavan Swaminathan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In using Machine Learning (ML) methods to predict S-parameters, handling the dimensionality problem of mapping the low-dimension design parameters to high-dimension responses is important. We propose to use batch training of Gaussian Process (GP) to map the design parameters into latent Gaussian space instead of linear mappings to create the non-linearity property as well as avoiding the saturation of activation functions before applying transposed kernels. Results show that the proposed model achieves better performance with regard to loss and normalized mean-squared error.

Original languageEnglish (US)
Title of host publicationEPEPS 2023 - IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350317985
DOIs
StatePublished - 2018
Event32nd IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2023 - Milpitas, United States
Duration: Oct 15 2023Oct 18 2023

Publication series

NameEPEPS 2023 - IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference32nd IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2023
Country/TerritoryUnited States
CityMilpitas
Period10/15/2310/18/23

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Electronic, Optical and Magnetic Materials

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