Monte Carlo estimation of sparse grid interpolation residual for stochastic collocation of high-order systems with uncertainties

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

Abstract

In fast sampling-based stochastic numerical techniques, Smolyak-based sparse grids are used to construct interpolation of the system output in random domain. The accuracy and convergence of sparse grid interpolations are evaluated by calculating the residual of the interpolation outputs compared with actual output values at grid nodes of higher levels. The residual is used as a criteria for adaptive refinement, as well as local refinement of sparse grids. In this paper, we propose a Monte Carlo sampling-based method to estimate the residual of sparse grid interpolations.

Original languageEnglish (US)
Title of host publication2017 IEEE 21st Workshop on Signal and Power Integrity, SPI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509056163
DOIs
StatePublished - Jun 7 2017
Event21st IEEE Workshop on Signal and Power Integrity, SPI 2017 - Lake Maggiore (Baveno), Italy
Duration: May 7 2017May 10 2017

Publication series

Name2017 IEEE 21st Workshop on Signal and Power Integrity, SPI 2017 - Proceedings

Other

Other21st IEEE Workshop on Signal and Power Integrity, SPI 2017
CountryItaly
CityLake Maggiore (Baveno)
Period5/7/175/10/17

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

Cite this

Chen, X., Ma, X., Rong, A., Schutt-Aine, J. E., & Cangellaris, A. C. (2017). Monte Carlo estimation of sparse grid interpolation residual for stochastic collocation of high-order systems with uncertainties. In 2017 IEEE 21st Workshop on Signal and Power Integrity, SPI 2017 - Proceedings [7944004] (2017 IEEE 21st Workshop on Signal and Power Integrity, SPI 2017 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SaPIW.2017.7944004