Analysis of chaotic dynamical systems with autoencoders
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| _version_ | 1866914838414884864 |
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| author | Almazova, N. Barmparis, G. D. Tsironis, G. P. |
| author_facet | Almazova, N. Barmparis, G. D. Tsironis, G. P. |
| contents | We focus on chaotic dynamical systems and analyze their time series with the use of autoencoders, i.e., configurations of neural networks that map identical output to input. This analysis results in the determination of the latent space dimension of each system and thus determines the minimal number of nodes necessary to capture the essential information contained in the chaotic time series. The constructed chaotic autoencoders generate similar maximal Lyapunov exponents as the original chaotic systems and thus encompass their essential dynamical information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_13078 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Analysis of chaotic dynamical systems with autoencoders Almazova, N. Barmparis, G. D. Tsironis, G. P. Neural and Evolutionary Computing Artificial Intelligence Chaotic Dynamics We focus on chaotic dynamical systems and analyze their time series with the use of autoencoders, i.e., configurations of neural networks that map identical output to input. This analysis results in the determination of the latent space dimension of each system and thus determines the minimal number of nodes necessary to capture the essential information contained in the chaotic time series. The constructed chaotic autoencoders generate similar maximal Lyapunov exponents as the original chaotic systems and thus encompass their essential dynamical information. |
| title | Analysis of chaotic dynamical systems with autoencoders |
| topic | Neural and Evolutionary Computing Artificial Intelligence Chaotic Dynamics |
| url | https://arxiv.org/abs/2109.13078 |