Rigorously Characterizing Dynamics with Machine Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911321106153472 |
|---|---|
| author | Gameiro, Marcio Gelb, Brittany Mischaikow, Konstantin |
| author_facet | Gameiro, Marcio Gelb, Brittany Mischaikow, Konstantin |
| contents | The identification of dynamics from time series data is a problem of general interest. It is well established that dynamics on the level of invariant sets, the primary objects of interest in the classical theory of dynamical systems, is not computable. We recall a coarser characterization of dynamics based on order theory and algebraic topology and prove that this characterization can be identified using approximations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17302 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rigorously Characterizing Dynamics with Machine Learning Gameiro, Marcio Gelb, Brittany Mischaikow, Konstantin Dynamical Systems 37B30, 68T07 The identification of dynamics from time series data is a problem of general interest. It is well established that dynamics on the level of invariant sets, the primary objects of interest in the classical theory of dynamical systems, is not computable. We recall a coarser characterization of dynamics based on order theory and algebraic topology and prove that this characterization can be identified using approximations. |
| title | Rigorously Characterizing Dynamics with Machine Learning |
| topic | Dynamical Systems 37B30, 68T07 |
| url | https://arxiv.org/abs/2505.17302 |