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Controlling nonlinear dynamical systems into arbitrary states using machine learning
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 as they...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8217470/ https://www.ncbi.nlm.nih.gov/pubmed/34155228 http://dx.doi.org/10.1038/s41598-021-92244-6 |