Enhancing Dynamic-Mode Decomposition via Multi-Scale Analysis
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912818907840512 |
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| author | Curtis, Christopher W. |
| author_facet | Curtis, Christopher W. |
| contents | Through the use of wavelet based Besov norms, we compute nontrivial multiscale nonlinear features of a given data set so as to enhance the standard Dynamic-Mode Decomposition algorithm. Thus we are able to build sophisticated observables which enhance algorithm performance without placing undue computational burdens on the user. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2001_02795 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Enhancing Dynamic-Mode Decomposition via Multi-Scale Analysis Curtis, Christopher W. Dynamical Systems Through the use of wavelet based Besov norms, we compute nontrivial multiscale nonlinear features of a given data set so as to enhance the standard Dynamic-Mode Decomposition algorithm. Thus we are able to build sophisticated observables which enhance algorithm performance without placing undue computational burdens on the user. |
| title | Enhancing Dynamic-Mode Decomposition via Multi-Scale Analysis |
| topic | Dynamical Systems |
| url | https://arxiv.org/abs/2001.02795 |