no code implementations • 12 Oct 2020 • Giorgos Mamakoukas, Maria L. Castano, Xiaobo Tan, Todd D. Murphey
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.