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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Autores principales: Alexander Haluszczynski, Christoph Räth
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/79566013c83c4808a31f467f4f6ba7ab
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