Abstract
We consider identification problems for stochastic nonlinear dynamical systems. An explicit sample complexity bound in terms of the number of data points required to recover the models to some certain precision is derived. Our results extend recent sample complexity results for linear stochastic dynamics. Our approach for obtaining sample complexity bounds for nonlinear dynamics relies on a linear, albeit infinite dimensional, representation of nonlinear dynamics provided by Koopman and Perron-Frobenius operators. We exploit the linear property of these operators to derive the sample complexity bounds. Such complexity bounds may play a significant role in data-driven learning and control of nonlinear dynamics. Several numerical examples are provided to highlight our theory.
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