Feature engineering to cope with noisy data in sparse identification

Dynamical systems play a fundamental role related understanding phenomena inherent to several fields of science. Technological advances over the previous several decades have generated a large amount of data that might be used in the inference of dynamical systems. Regardless of the sensor types adopted to perform the data acquisition procedure, it is useful to verify the existence of certain noise corruption in data. Generically, system identification is directly affected by noisy scenarios, which result in the false discovery of non-spurious models. In this work, we demonstrate how the hybridization of several machine learning techniques improves the robustness to noise with respect to the system identification assignment, advancing a pioneer methodology known as Sparse Identification of Nonlinear Dynamics (SINDy). Specifically, in the current work, we show the success of the proposed strategy from numerical examples, such as a logistic equation with forcing, Duffing oscillator, FitzHugh–Nagumo model, Lorenz attractor and a Susceptible–Infectious–Recovered modeling of SARS-CoV-2. •A robustness to noise version of the Sparse Identification.•A feature engineering approach to improve the identification task.•A Hybridization of machine learning techniques.