Dynamic mode decomposition of noisy flow data
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912209225908224 |
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| author | Weiner, Andre Geise, Janis |
| author_facet | Weiner, Andre Geise, Janis |
| contents | Dynamic mode decomposition (DMD) is a popular approach to analyzing and modeling fluid flows. In practice, datasets are almost always corrupted to some degree by noise. The vanilla DMD is highly noise-sensitive, which is why many algorithmic extensions for improved robustness exist. We introduce a flexible optimization approach that merges available ideas for improved accuracy and robustness. The approach simultaneously identifies coherent dynamics and noise in the data. In tests on the laminar flow past a cylinder, the method displays strong noise robustness and high levels of accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04868 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic mode decomposition of noisy flow data Weiner, Andre Geise, Janis Fluid Dynamics Dynamic mode decomposition (DMD) is a popular approach to analyzing and modeling fluid flows. In practice, datasets are almost always corrupted to some degree by noise. The vanilla DMD is highly noise-sensitive, which is why many algorithmic extensions for improved robustness exist. We introduce a flexible optimization approach that merges available ideas for improved accuracy and robustness. The approach simultaneously identifies coherent dynamics and noise in the data. In tests on the laminar flow past a cylinder, the method displays strong noise robustness and high levels of accuracy. |
| title | Dynamic mode decomposition of noisy flow data |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2411.04868 |