Before revision: This paper presents a methodology for linear embedding of nonlinear systems that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states.
After revision: This paper presents a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states.