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- W3174433017 abstract "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 as they rely on phase space methods. In this work we propose a novel and fully data driven scheme relying on machine learning (ML), which generalizes control techniques of chaotic systems without requiring a mathematical model for its dynamics. Exploiting recently developed ML-based prediction capabilities, we demonstrate that nonlinear systems can be forced to stay in arbitrary dynamical target states coming from any initial state. We outline and validate our approach using the examples of the Lorenz and the Rössler system and show how these systems can very accurately be brought not only to periodic, but even to intermittent and different chaotic behavior. Having this highly flexible control scheme with little demands on the amount of required data on hand, we briefly discuss possible applications ranging from engineering to medicine." @default.
- W3174433017 created "2021-07-05" @default.
- W3174433017 creator A5054337245 @default.
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- W3174433017 date "2021-06-21" @default.
- W3174433017 modified "2023-10-11" @default.
- W3174433017 title "Controlling nonlinear dynamical systems into arbitrary states using machine learning" @default.
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- W3174433017 doi "https://doi.org/10.1038/s41598-021-92244-6" @default.
- W3174433017 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8217470" @default.
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- W3174433017 hasPublicationYear "2021" @default.
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