Data-driven reduced order modeling for mechanical oscillators using Koopman approaches

Data-driven reduced order modeling methods that aim at extracting physically meaningful governing equations directly from measurement data are facing a growing interest in recent years. The HAVOK-algorithm is a Koopman-based method that distills a forced, low-dimensional state-space model for a give...

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Bibliographic Details
Published inFrontiers in applied mathematics and statistics Vol. 9
Main Authors Geier, Charlotte, Stender, Merten, Hoffmann, Norbert
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 28.04.2023
Subjects
modal analysis
sparse measurement data
state-space embeddings
state-space models
structural dynamics
system identification
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Summary:Data-driven reduced order modeling methods that aim at extracting physically meaningful governing equations directly from measurement data are facing a growing interest in recent years. The HAVOK-algorithm is a Koopman-based method that distills a forced, low-dimensional state-space model for a given dynamical system from a univariate measurement time series. This article studies the potential of HAVOK for application to mechanical oscillators by investigating which information of the underlying system can be extracted from the state-space model generated by HAVOK. Extensive parameter studies are performed to point out the strengths and pitfalls of the algorithm and ultimately yield recommendations for choosing tuning parameters. The application of the algorithm to real-world friction brake system measurements concludes this study.
ISSN:2297-4687
2297-4687
DOI:10.3389/fams.2023.1124602