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.