An Online Method for Interpolating Linear Parametric Reduced-Order Models

David Amsallem, Charbel Farhat

Research output: Contribution to journalArticlepeer-review

226 Scopus citations


A two-step online method is proposed for interpolating projection-based linear parametric reduced-order models (ROMs) in order to construct a new ROM for a new set of parameter values. The first step of this method transforms each precomputed ROM into a consistent set of generalized coordinates. The second step interpolates the associated linear operators on their appropriate matrix manifold. Real-time performance is achieved by precomputing inner products between the reduced-order bases underlying the precomputed ROMs. The proposed method is illustrated by applications in mechanical and aeronautical engineering. In particular, its robustness is demonstrated by its ability to handle the case where the sampled parameter set values exhibit a mode veering phenomenon. © 2011 Society for Industrial and Applied Mathematics.
Original languageEnglish (US)
Pages (from-to)2169-2198
Number of pages30
JournalSIAM Journal on Scientific Computing
Issue number5
StatePublished - Jan 2011
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Submitted to the journal's Computational Methods in Science and Engineering section October 27, 2010; accepted for publication (in revised form) June 3, 2011; published electronically September 1, 2011. This work was partially supported by a research grant from the Academic Excellence Alliance program between King Abdullah University of Science and Technology (KAUST) and Stanford University, by a research grant from King Abdulaziz City for Science and Technology (KACST), by The Boeing Company under contract 45047, and by the Air Force Office of Scientific Research under grant FA9550-10-1-0539.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'An Online Method for Interpolating Linear Parametric Reduced-Order Models'. Together they form a unique fingerprint.

Cite this