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• W2017990410 abstract "Existing methods of time series analysis of nonlinear dynamical systems deal with the dynamics on single (strange) attractors. We extend this method for systems that can be externally manipulated. We interact with a previously unknown system by perturbing it randomly and recording the responses. By following transients for a short time and approximating the global flow, we can predict the final long-term dynamics, including coexisting multistable, chaotic, or periodic dynamics. Also the localization of basins of attraction is possible, enabling the selection or control of the type of final dynamics. Numerical and experimental examples of driven nonlinear oscillators are given." @default.
• W2017990410 created "2016-06-24" @default.
• W2017990410 creator A5017226670 @default.
• W2017990410 date "2005-01-13" @default.
• W2017990410 modified "2023-09-30" @default.
• W2017990410 title "Prediction of long-term dynamics from transients" @default.
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• W2017990410 doi "https://doi.org/10.1103/physreve.71.016214" @default.
• W2017990410 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15697705" @default.
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