Towards an Adaptive Dynamic Mode Decomposition
Dynamic Mode Decomposition (DMD) is a tool that creates an approximate model from spatio-temporal data. We have developed an architecture of this tool that will adapt to the data from a given problem by leveraging time delay coordinates, projections, and robust principal component analysis. Our sche...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2022
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/cf5ce29168784f3ebae8f29cb4fca29f |
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| Sumario: | Dynamic Mode Decomposition (DMD) is a tool that creates an approximate model from spatio-temporal data. We have developed an architecture of this tool that will adapt to the data from a given problem by leveraging time delay coordinates, projections, and robust principal component analysis. Our scheme which we call Adaptive Dynamic Mode Decomposition (ADMD) can be used in its exact form or the user may even utilize parts of the scheme for generating a DMD model that is more accurate and reliable compared to the one given by standard DMD. ADMD is demonstrated on several datasets of varying complexities and its performance appears to be promising. |
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