Dynamical systems and complex networks: a Koopman operator perspective

The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept—representing highly nonlinear dynamical systems by infinite-dimensional linear operators—has been known for a long time, the availability of large data sets and efficient machine...

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Main Authors: Stefan Klus, Nataša Djurdjevac Conrad
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Journal of Physics: Complexity
Subjects:
Online Access:https://doi.org/10.1088/2632-072X/ad9e60
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author Stefan Klus
Nataša Djurdjevac Conrad
author_facet Stefan Klus
Nataša Djurdjevac Conrad
author_sort Stefan Klus
collection DOAJ
description The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept—representing highly nonlinear dynamical systems by infinite-dimensional linear operators—has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians.
format Article
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issn 2632-072X
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publishDate 2024-01-01
publisher IOP Publishing
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series Journal of Physics: Complexity
spelling doaj-art-20757fa38cbb4348a6735fe7ca3b2d2f2025-08-20T02:32:29ZengIOP PublishingJournal of Physics: Complexity2632-072X2024-01-015404100110.1088/2632-072X/ad9e60Dynamical systems and complex networks: a Koopman operator perspectiveStefan Klus0https://orcid.org/0000-0002-9672-3806Nataša Djurdjevac Conrad1School of Mathematical & Computer Sciences, Heriot–Watt University , Edinburgh, United KingdomZuse Institute Berlin , Berlin, GermanyThe Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept—representing highly nonlinear dynamical systems by infinite-dimensional linear operators—has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians.https://doi.org/10.1088/2632-072X/ad9e60Koopman operatorgraphs and networksspectral clustering
spellingShingle Stefan Klus
Nataša Djurdjevac Conrad
Dynamical systems and complex networks: a Koopman operator perspective
Journal of Physics: Complexity
Koopman operator
graphs and networks
spectral clustering
title Dynamical systems and complex networks: a Koopman operator perspective
title_full Dynamical systems and complex networks: a Koopman operator perspective
title_fullStr Dynamical systems and complex networks: a Koopman operator perspective
title_full_unstemmed Dynamical systems and complex networks: a Koopman operator perspective
title_short Dynamical systems and complex networks: a Koopman operator perspective
title_sort dynamical systems and complex networks a koopman operator perspective
topic Koopman operator
graphs and networks
spectral clustering
url https://doi.org/10.1088/2632-072X/ad9e60
work_keys_str_mv AT stefanklus dynamicalsystemsandcomplexnetworksakoopmanoperatorperspective
AT natasadjurdjevacconrad dynamicalsystemsandcomplexnetworksakoopmanoperatorperspective