SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study
This work addresses challenges in applying SINDy to real dynamical systems, particularly in control applications, but is incremental as it builds on existing methods.
The study evaluated the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, revealing difficulties in handling unobserved states and non-smooth dynamics, and proposed hands-on approaches to address these issues for real-world applications.
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.