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The SINDy algorithm has been successfully used to identify the governingequations of dynamical systems from time series data. However, SINDy assumesthe user has prior knowledge of the variables in the system and of a functionlibrary that can act as a basis for the system. In this paper, we demonstrateon real world data how the Augmented SINDy algorithm outperforms SINDy in thepresence of system variable uncertainty. We then show SINDy can be furtheraugmented to perform robustly when both kinds of uncertainty are present.

Sparse identification of nonlinear dynamics in the presence of library and system uncertainty