Data assimilation for networks of coupled oscillators: Inferring unknown model parameters from partial observations
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866910903018979328 |
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| author | Smith, Lauren D. Gottwald, Georg A. |
| author_facet | Smith, Lauren D. Gottwald, Georg A. |
| contents | Inferring the state and unknown parameters of a network of coupled oscillators is of utmost importance. This task is made harder when only partial and noisy observations are available, which is a typical scenario in realistic high-dimensional systems. The general task of inference falls under data assimilation, and a commonly used assimilation method is the Ensemble Kalman Filter. Employing network-specific localization of the forecast covariance, an Ensemble Kalman Filter with state space augmentation is shown to yield highly accurate estimates of both the oscillator phases and unknown model parameters in the case where only a subset of oscillator phases are observed. In contrast, standard data assimilation methods yield poor results. We demonstrate the effectiveness of our approach for Kuramoto oscillators and for networks of theta neurons, using a variety of network topologies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_03545 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data assimilation for networks of coupled oscillators: Inferring unknown model parameters from partial observations Smith, Lauren D. Gottwald, Georg A. Adaptation and Self-Organizing Systems Disordered Systems and Neural Networks Dynamical Systems Data Analysis, Statistics and Probability Inferring the state and unknown parameters of a network of coupled oscillators is of utmost importance. This task is made harder when only partial and noisy observations are available, which is a typical scenario in realistic high-dimensional systems. The general task of inference falls under data assimilation, and a commonly used assimilation method is the Ensemble Kalman Filter. Employing network-specific localization of the forecast covariance, an Ensemble Kalman Filter with state space augmentation is shown to yield highly accurate estimates of both the oscillator phases and unknown model parameters in the case where only a subset of oscillator phases are observed. In contrast, standard data assimilation methods yield poor results. We demonstrate the effectiveness of our approach for Kuramoto oscillators and for networks of theta neurons, using a variety of network topologies. |
| title | Data assimilation for networks of coupled oscillators: Inferring unknown model parameters from partial observations |
| topic | Adaptation and Self-Organizing Systems Disordered Systems and Neural Networks Dynamical Systems Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2309.03545 |