Sparse identification of nonlinear dynamics in the presence of library and system uncertainty
This work addresses a specific limitation in system identification for researchers in dynamical systems, but it is incremental as it builds directly on the existing SINDy algorithm.
The paper tackles the problem of identifying governing equations of dynamical systems from time series data when there is uncertainty in system variables and function libraries, showing that Augmented SINDy outperforms SINDy in such scenarios and can be made robust to both types of uncertainty.
The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. However, SINDy assumes the user has prior knowledge of the variables in the system and of a function library that can act as a basis for the system. In this paper, we demonstrate on real world data how the Augmented SINDy algorithm outperforms SINDy in the presence of system variable uncertainty. We then show SINDy can be further augmented to perform robustly when both kinds of uncertainty are present.