  In this paper, we propose a data-driven approach for uncertainty propagation
and reachability analysis in a dynamical system. The proposed approach relies
on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F)
and Koopman operators. The uncertainty can be characterized in terms of the
moments of a probability density function. We demonstrate how the P-F and
Koopman operators are used for propagating the moments. Time-series data is
used for the finite-dimensional approximation of the linear operators, thereby
enabling data-driven approach for moment propagation. Simulation results are
presented to demonstrate the effectiveness of the proposed method.
