Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.14951 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929429249261568 |
|---|---|
| author | Sisodia, Dishant Jalan, Sarika |
| author_facet | Sisodia, Dishant Jalan, Sarika |
| contents | Reservoir computing has been shown to be a useful framework for predicting critical transitions of a dynamical system if the bifurcation parameter is also provided as an input. Its utility is significant because in real-world scenarios, the exact model equations are unknown. This Letter shows how the theory of dynamical system provides the underlying mechanism behind the prediction. Using numerical methods, by considering dynamical systems which show Hopf bifurcation, we demonstrate that the map produced by the reservoir after a successful training undergoes a Neimark-Sacker bifurcation such that the critical point of the map is in immediate proximity to that of the original dynamical system. In addition, we have compared and analyzed different structures in the phase space. Our findings provide insight into the functioning of machine learning algorithms for predicting critical transitions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14951 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamical analysis of a parameter-aware reservoir computer Sisodia, Dishant Jalan, Sarika Adaptation and Self-Organizing Systems Computational Physics Reservoir computing has been shown to be a useful framework for predicting critical transitions of a dynamical system if the bifurcation parameter is also provided as an input. Its utility is significant because in real-world scenarios, the exact model equations are unknown. This Letter shows how the theory of dynamical system provides the underlying mechanism behind the prediction. Using numerical methods, by considering dynamical systems which show Hopf bifurcation, we demonstrate that the map produced by the reservoir after a successful training undergoes a Neimark-Sacker bifurcation such that the critical point of the map is in immediate proximity to that of the original dynamical system. In addition, we have compared and analyzed different structures in the phase space. Our findings provide insight into the functioning of machine learning algorithms for predicting critical transitions. |
| title | Dynamical analysis of a parameter-aware reservoir computer |
| topic | Adaptation and Self-Organizing Systems Computational Physics |
| url | https://arxiv.org/abs/2407.14951 |