Sparse identification of nonlinear dynamics in the presence of library and system uncertainty
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910306433761280 |
|---|---|
| author | O'Brien, Andrew |
| author_facet | O'Brien, Andrew |
| contents | The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. However, SINDy assumes the user has prior knowledge of the variables in the system and of a function library that can act as a basis for the system. In this paper, we demonstrate on real world data how the Augmented SINDy algorithm outperforms SINDy in the presence of system variable uncertainty. We then show SINDy can be further augmented to perform robustly when both kinds of uncertainty are present. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13099 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse identification of nonlinear dynamics in the presence of library and system uncertainty O'Brien, Andrew Machine Learning Artificial Intelligence The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. However, SINDy assumes the user has prior knowledge of the variables in the system and of a function library that can act as a basis for the system. In this paper, we demonstrate on real world data how the Augmented SINDy algorithm outperforms SINDy in the presence of system variable uncertainty. We then show SINDy can be further augmented to perform robustly when both kinds of uncertainty are present. |
| title | Sparse identification of nonlinear dynamics in the presence of library and system uncertainty |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.13099 |