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

Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems