Concurrent multi-parameter learning demonstrated on the Kuramoto-Sivashinsky equation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866916792213962752 |
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| author | Pachev, Benjamin Whitehead, Jared P. McQuarrie, Shane A. |
| author_facet | Pachev, Benjamin Whitehead, Jared P. McQuarrie, Shane A. |
| contents | We develop an algorithm based on the nudging data assimilation scheme for the concurrent (on-the-fly) estimation of scalar parameters for a system of evolutionary dissipative partial differential equations in which the state is partially observed. The algorithm takes advantage of the error that results from nudging a system with incorrect parameters with data from the true system. The intuitive nature of the algorithm makes its extension to several different systems immediate, and it allows for recovery of multiple parameters simultaneously. We test the method on the Kuramoto-Sivashinsky equation in one dimension and demonstrate its efficacy in this context. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_06069 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Concurrent multi-parameter learning demonstrated on the Kuramoto-Sivashinsky equation Pachev, Benjamin Whitehead, Jared P. McQuarrie, Shane A. Numerical Analysis Dynamical Systems Chaotic Dynamics 35F20, 35R30, 65M32, 65M70 We develop an algorithm based on the nudging data assimilation scheme for the concurrent (on-the-fly) estimation of scalar parameters for a system of evolutionary dissipative partial differential equations in which the state is partially observed. The algorithm takes advantage of the error that results from nudging a system with incorrect parameters with data from the true system. The intuitive nature of the algorithm makes its extension to several different systems immediate, and it allows for recovery of multiple parameters simultaneously. We test the method on the Kuramoto-Sivashinsky equation in one dimension and demonstrate its efficacy in this context. |
| title | Concurrent multi-parameter learning demonstrated on the Kuramoto-Sivashinsky equation |
| topic | Numerical Analysis Dynamical Systems Chaotic Dynamics 35F20, 35R30, 65M32, 65M70 |
| url | https://arxiv.org/abs/2106.06069 |