  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.
