Paper ID: 2403.00578

SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study

Aurelio Raffa Ugolini, Valentina Breschi, Andrea Manzoni, Mara Tanelli

In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems. While SINDy can be an appealing strategy for pursuing physics-based learning, our analysis highlights difficulties in dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general, and control applications in particular, we complement our analysis with hands-on approaches to tackle these issues in order to exploit SINDy also in these challenging contexts.

Submitted: Mar 1, 2024