Latent Diffeomorphic Dynamic Mode Decomposition
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916874469507072 |
|---|---|
| author | Diepeveen, Willem Schwenk, Jon Bertozzi, Andrea |
| author_facet | Diepeveen, Willem Schwenk, Jon Bertozzi, Andrea |
| contents | We present Latent Diffeomorphic Dynamic Mode Decomposition (LDDMD), a new data reduction approach for the analysis of non-linear systems that combines the interpretability of Dynamic Mode Decomposition (DMD) with the predictive power of Recurrent Neural Networks (RNNs). Notably, LDDMD maintains simplicity, which enhances interpretability, while effectively modeling and learning complex non-linear systems with memory, enabling accurate predictions. This is exemplified by its successful application in streamflow prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent Diffeomorphic Dynamic Mode Decomposition Diepeveen, Willem Schwenk, Jon Bertozzi, Andrea Machine Learning Dynamical Systems We present Latent Diffeomorphic Dynamic Mode Decomposition (LDDMD), a new data reduction approach for the analysis of non-linear systems that combines the interpretability of Dynamic Mode Decomposition (DMD) with the predictive power of Recurrent Neural Networks (RNNs). Notably, LDDMD maintains simplicity, which enhances interpretability, while effectively modeling and learning complex non-linear systems with memory, enabling accurate predictions. This is exemplified by its successful application in streamflow prediction. |
| title | Latent Diffeomorphic Dynamic Mode Decomposition |
| topic | Machine Learning Dynamical Systems |
| url | https://arxiv.org/abs/2505.06351 |