Identifying Network Structure of Nonlinear Dynamical Systems: Contraction and Kuramoto Oscillators
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908880772005888 |
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| author | Gill, Jaidev Li, Jing Shuang |
| author_facet | Gill, Jaidev Li, Jing Shuang |
| contents | In this work, we study the identifiability of network structures (i.e., topologies) for networked nonlinear systems when partial measurements of the nodal dynamics are taken. We explore scenarios where different candidate structures can yield similar measurements, thus limiting identifiability. To do so, we apply the contraction theory framework to facilitate comparisons between different networks. We show that semicontraction in the observable space is a sufficient condition for two systems to become indistinguishable from one another based on partial measurements. We apply this framework to study networks of Kuramoto oscillators, and discuss scenarios in which different network structures (both connected and disconnected) become indistinguishable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Network Structure of Nonlinear Dynamical Systems: Contraction and Kuramoto Oscillators Gill, Jaidev Li, Jing Shuang Systems and Control Optimization and Control In this work, we study the identifiability of network structures (i.e., topologies) for networked nonlinear systems when partial measurements of the nodal dynamics are taken. We explore scenarios where different candidate structures can yield similar measurements, thus limiting identifiability. To do so, we apply the contraction theory framework to facilitate comparisons between different networks. We show that semicontraction in the observable space is a sufficient condition for two systems to become indistinguishable from one another based on partial measurements. We apply this framework to study networks of Kuramoto oscillators, and discuss scenarios in which different network structures (both connected and disconnected) become indistinguishable. |
| title | Identifying Network Structure of Nonlinear Dynamical Systems: Contraction and Kuramoto Oscillators |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2509.13505 |