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.

翻译：SINDy算法已成功用于从时间序列数据中辨识动力系统的控制方程。然而，SINDy假设用户具备系统变量的先验知识，以及可作为系统基底的函数库。本文通过真实数据展示，在存在系统变量不确定性时，增强型SINDy算法如何优于SINDy。随后我们证明，当两种不确定性同时存在时，SINDy可被进一步增强以稳健运行。