Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.15732 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929387598774272 |
|---|---|
| author | Valle, David Wagemakers, Alexandre Sanjuán, Miguel A. F. |
| author_facet | Valle, David Wagemakers, Alexandre Sanjuán, Miguel A. F. |
| contents | This research addresses the challenge of characterizing the complexity and unpredictability of basins within various dynamical systems. The main focus is on demonstrating the efficiency of convolutional neural networks (CNNs) in this field. Conventional methods become computationally demanding when analyzing multiple basins of attraction across different parameters of dynamical systems. Our research presents an innovative approach that employs CNN architectures for this purpose, showcasing their superior performance in comparison to conventional methods. We conduct a comparative analysis of various CNN models, highlighting the effectiveness of our proposed characterization method while acknowledging the validity of prior approaches. The findings not only showcase the potential of CNNs but also emphasize their significance in advancing the exploration of diverse behaviors within dynamical systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15732 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Deep Learning-based Analysis of Basins of Attraction Valle, David Wagemakers, Alexandre Sanjuán, Miguel A. F. Machine Learning This research addresses the challenge of characterizing the complexity and unpredictability of basins within various dynamical systems. The main focus is on demonstrating the efficiency of convolutional neural networks (CNNs) in this field. Conventional methods become computationally demanding when analyzing multiple basins of attraction across different parameters of dynamical systems. Our research presents an innovative approach that employs CNN architectures for this purpose, showcasing their superior performance in comparison to conventional methods. We conduct a comparative analysis of various CNN models, highlighting the effectiveness of our proposed characterization method while acknowledging the validity of prior approaches. The findings not only showcase the potential of CNNs but also emphasize their significance in advancing the exploration of diverse behaviors within dynamical systems. |
| title | Deep Learning-based Analysis of Basins of Attraction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2309.15732 |