SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study
CoRR(2024)
摘要
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.
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