Prediction and control of spatiotemporal chaos by learning conjugate tubular neighborhoods
I present a data-driven predictive modeling tool that is applicable to high-dimensional chaotic systems with unstable periodic orbits. The basic idea is using deep neural networks to learn coordinate transformations between the trajectories in the periodic orbits’ neighborhoods and those of low-dime...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2024-03-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0181022 |