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Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization

Baddoo, Peter J. and Herrmann, Benjamin and McKeon, Beverley J. and Brunton, Steven L. (2022) Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization. Proceedings of the Royal Society A: Mathematical, physical, and engineering sciences, 478 (2260). Art. No. 2021.0830. ISSN 1364-5021. PMCID PMC9006118. doi:10.1098/rspa.2021.0830. https://resolver.caltech.edu/CaltechAUTHORS:20220414-26938000

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Abstract

Research in modern data-driven dynamical systems is typically focused on the three key challenges of high dimensionality, unknown dynamics and nonlinearity. The dynamic mode decomposition (DMD) has emerged as a cornerstone for modelling high-dimensional systems from data. However, the quality of the linear DMD model is known to be fragile with respect to strong nonlinearity, which contaminates the model estimate. By contrast, sparse identification of nonlinear dynamics learns fully nonlinear models, disambiguating the linear and nonlinear effects, but is restricted to low-dimensional systems. In this work, we present a kernel method that learns interpretable data-driven models for high-dimensional, nonlinear systems. Our method performs kernel regression on a sparse dictionary of samples that appreciably contribute to the dynamics. We show that this kernel method efficiently handles high-dimensional data and is flexible enough to incorporate partial knowledge of system physics. It is possible to recover the linear model contribution with this approach, thus separating the effects of the implicitly defined nonlinear terms. We demonstrate our approach on data from a range of nonlinear ordinary and partial differential equations. This framework can be used for many practical engineering tasks such as model order reduction, diagnostics, prediction, control and discovery of governing laws.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1098/rspa.2021.0830DOIArticle
http://www.ncbi.nlm.nih.gov/pmc/articles/pmc9006118/PubMed CentralArticle
https://www.github.com/baddoo/LANDORelated ItemData and code
https://arxiv.org/abs/2106.01510arXivDiscussion Paper
ORCID:
AuthorORCID
Baddoo, Peter J.0000-0002-8671-6952
McKeon, Beverley J.0000-0003-4220-1583
Brunton, Steven L.0000-0002-6565-5118
Additional Information:© 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. Manuscript received 28/10/2021. Manuscript accepted 28/02/2022. Published online 13/04/2022. Published in print 27/04/2022. S.L.B. would like to thank Bing Brunton, Nathan Kutz, Jean-Christophe Loiseau and Isabel Scherl for valuable discussions. This work was supported by U.S. Army Research Office (ARO W911NF-17-1-0306 and ARO W911NF-19-1-0045), the U.S. Office of Naval Research (ONR N00014-17-1-3022) and by the PRIME programme of the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (BMBF). Data accessibility. Further information is provided in the electronic supplementary material [73]. Data and codes are available at www.github.com/baddoo/LANDO. Authors' contributions. P.J.B.: conceptualization, formal analysis, investigation, methodology, visualization, writing–original draft, writing—review and editing; B.H.: methodology, writing—original draft, writing—review and editing; B.J.M.: conceptualization, funding acquisition, supervision, writing—original draft, writing—review and editing; S.L.B.: conceptualization, funding acquisition, methodology, supervision, visualization, writing—original draft, writing—review and editing. All authors gave final approval for publication and agreed to be held accountable for the work performed therein. We declare we have no competing interests.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Army Research Office (ARO)W911NF-17-1-0306
Army Research OfficeW911NF-19-1-0045
Office of Naval Research (ONR)N00014-17-1-3022
Deutscher Akademischer Austauschdienst (DAAD)UNSPECIFIED
Bundesministerium für Bildung und Forschung (BMBF)UNSPECIFIED
Subject Keywords:machine learning; kernel methods; modal decomposition; system identification
Issue or Number:2260
PubMed Central ID:PMC9006118
DOI:10.1098/rspa.2021.0830
Record Number:CaltechAUTHORS:20220414-26938000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220414-26938000
Official Citation:Baddoo PJ, Herrmann B, McKeon BJ, Brunton SL. Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization. Proc Math Phys Eng Sci. 2022 Apr;478(2260):20210830. doi: 10.1098/rspa.2021.0830
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114317
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:18 Apr 2022 19:18
Last Modified:25 Apr 2022 18:19

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