Controlling Transient Chaos in the Lorenz System with Machine Learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916667494236160 |
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| author | Valle, David Capeans, Rubén Wagemakers, Alexandre Sanjuán, Miguel A. F. |
| author_facet | Valle, David Capeans, Rubén Wagemakers, Alexandre Sanjuán, Miguel A. F. |
| contents | This paper presents a novel approach to sustain transient chaos in the Lorenz system through the estimation of safety functions using a transformer-based model. Unlike classical methods that rely on iterative computations, the proposed model directly predicts safety functions without requiring fine-tuning or extensive system knowledge. The results demonstrate that this approach effectively maintains chaotic trajectories within the desired phase space region, even in the presence of noise, making it a viable alternative to traditional methods. A detailed comparison of safety functions, safe sets, and their control performance highlights the strengths and trade-offs of the two approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17588 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Controlling Transient Chaos in the Lorenz System with Machine Learning Valle, David Capeans, Rubén Wagemakers, Alexandre Sanjuán, Miguel A. F. Chaotic Dynamics This paper presents a novel approach to sustain transient chaos in the Lorenz system through the estimation of safety functions using a transformer-based model. Unlike classical methods that rely on iterative computations, the proposed model directly predicts safety functions without requiring fine-tuning or extensive system knowledge. The results demonstrate that this approach effectively maintains chaotic trajectories within the desired phase space region, even in the presence of noise, making it a viable alternative to traditional methods. A detailed comparison of safety functions, safe sets, and their control performance highlights the strengths and trade-offs of the two approaches. |
| title | Controlling Transient Chaos in the Lorenz System with Machine Learning |
| topic | Chaotic Dynamics |
| url | https://arxiv.org/abs/2501.17588 |