A Modified Polynomial Chaos Modeling Approach for Uncertainty Quantification

Majid Ahadi Dolatsara, Ambrish Varma, Kumar Keshavan, Madhavan Swaminathan

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

2 Scopus citations

Abstract

Uncertainty quantification is a key element for modeling high speed circuits, which is often done with Monte Carlo analysis. However, because this method is computationally expensive, new approaches with higher efficiency have been developed. Many popular methods are based on the surrogate models developed with Polynomial Chaos theory. However, size of these models can be prohibitively large for realistic examples. Hence, this paper provides a novel methodology to use an ensemble of weaker models to improve efficiency. Finally, a numerical example with a DDR4 topology is provided.

Original languageEnglish (US)
Title of host publication2019 International Applied Computational Electromagnetics Society Symposium in Miami, ACES-Miami 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780996007887
StatePublished - May 10 2019
Event2019 International Applied Computational Electromagnetics Society Symposium in Miami, ACES-Miami 2019 - Miami, United States
Duration: Apr 14 2019Apr 18 2019

Publication series

Name2019 International Applied Computational Electromagnetics Society Symposium in Miami, ACES-Miami 2019

Conference

Conference2019 International Applied Computational Electromagnetics Society Symposium in Miami, ACES-Miami 2019
Country/TerritoryUnited States
CityMiami
Period4/14/194/18/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computational Mathematics
  • Instrumentation
  • Radiation

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