Prediction of chaotic dynamics from data: An introduction
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910125587955712 |
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| author | Magri, Luca Nóvoa, Andrea Özalp, Elise |
| author_facet | Magri, Luca Nóvoa, Andrea Özalp, Elise |
| contents | This chapter offers a principled approach to the prediction of chaotic systems from data. First, we introduce some concepts from dynamical systems' theory and chaos theory. Second, we introduce machine learning approaches for time-forecasting chaotic dynamics, such as echo state networks and long-short-term memory networks, whilst keeping a dynamical systems' perspective. Third, the lecture contains informal interpretations and pedagogical examples with prototypical chaotic systems (e.g., the Lorenz system), which elucidate the theory. The chapter is complemented by coding tutorials (online) at https://github.com/MagriLab/Tutorials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11624 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Prediction of chaotic dynamics from data: An introduction Magri, Luca Nóvoa, Andrea Özalp, Elise Chaotic Dynamics This chapter offers a principled approach to the prediction of chaotic systems from data. First, we introduce some concepts from dynamical systems' theory and chaos theory. Second, we introduce machine learning approaches for time-forecasting chaotic dynamics, such as echo state networks and long-short-term memory networks, whilst keeping a dynamical systems' perspective. Third, the lecture contains informal interpretations and pedagogical examples with prototypical chaotic systems (e.g., the Lorenz system), which elucidate the theory. The chapter is complemented by coding tutorials (online) at https://github.com/MagriLab/Tutorials. |
| title | Prediction of chaotic dynamics from data: An introduction |
| topic | Chaotic Dynamics |
| url | https://arxiv.org/abs/2604.11624 |