| Abstract: |
| Dynamical systems are usually analysed using standard techniques such as Lyapunov exponents. However, most of these classical methods are computationally expensive and often not feasible for studying real-world data. In this communication, we propose to use Deep Learning to overcome such limitations. We apply Deep Learning to detect chaotic regions in the parameter space of classical dynamical systems and to analyse chaotic dynamics in biological time series like experimental frog heart data. Furthermore, we use Deep Learning to detect more complex dynamical regimes by approximating Lyapunov exponents from single-variable time series. These analyses show the potential of Deep Learning in the study of dynamical systems. |
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