  We consider the identification problems for nonlinear dynamical systems. An
explicit sample complexity bound in terms of the number of data points required
to recover the models accurately is derived. Our results extend recent sample
complexity results for linear 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 operator. We exploit the linear property of these operators to
derive the sample complexity bounds. Such complexity bounds will play a
significant role in data-driven learning and control of nonlinear dynamics.
Several numerical examples are provided to highlight our theory.
