Controlling nonlinear dynamical systems into arbitrary states using machine learning
Abstract Controlling nonlinear dynamical systems is a central task in many different areas of science and engineering. Chaotic systems can be stabilized (or chaotified) with small perturbations, yet existing approaches either require knowledge about the underlying system equations or large data sets...
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| Auteurs principaux: | , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/79566013c83c4808a31f467f4f6ba7ab |
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