Recovering the Parameter $α$ in the Simplified Bardina Model through Continuous Data Assimilation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917099669028864 |
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| author | Albanez, Débora A. F. Benvenutti, Maicon José Tian, Jing |
| author_facet | Albanez, Débora A. F. Benvenutti, Maicon José Tian, Jing |
| contents | In this study, we develop a continuous data assimilation algorithm to recover the parameter $α$ in the simplified Bardina model. Our method utilizes the observations of finitely many Fourier modes by using a nudging framework that involves recursive parameter updates. We provide a rigorous convergence analysis, showing that the approximate parameter approaches the true value under suitable conditions, while the approximate solution also converges to the true solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recovering the Parameter $α$ in the Simplified Bardina Model through Continuous Data Assimilation Albanez, Débora A. F. Benvenutti, Maicon José Tian, Jing Dynamical Systems In this study, we develop a continuous data assimilation algorithm to recover the parameter $α$ in the simplified Bardina model. Our method utilizes the observations of finitely many Fourier modes by using a nudging framework that involves recursive parameter updates. We provide a rigorous convergence analysis, showing that the approximate parameter approaches the true value under suitable conditions, while the approximate solution also converges to the true solution. |
| title | Recovering the Parameter $α$ in the Simplified Bardina Model through Continuous Data Assimilation |
| topic | Dynamical Systems |
| url | https://arxiv.org/abs/2511.08421 |