A black-box method for parametric model order reduction

M. Geuss, B. Lohmann, B. Peherstorfer, K. Willcox

Research output: Contribution to journalConference article

Abstract

A black-box method for parametric model order reduction is presented that includes method selection, model refinement and error prediction using a cross-validation-based error indicator. The method is demonstrated for the interpolation of reduced system matrices.

Original languageEnglish (US)
Pages (from-to)168-169
Number of pages2
JournalIFAC-PapersOnLine
Volume28
Issue number1
DOIs
StatePublished - Feb 1 2015
Event8th Vienna International Conference on Mathematical Modelling, MATHMOD 2015 - Vienna, Austria
Duration: Feb 18 2015Feb 20 2015

Keywords

  • Cross-validation
  • Error prediction
  • Matrix interpolation
  • Method selection
  • Model refinement
  • Parametric model order reduction
  • Surrogate model

ASJC Scopus subject areas

  • Control and Systems Engineering

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  • Cite this

    Geuss, M., Lohmann, B., Peherstorfer, B., & Willcox, K. (2015). A black-box method for parametric model order reduction. IFAC-PapersOnLine, 28(1), 168-169. https://doi.org/10.1016/j.ifacol.2015.05.131