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A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study
In this work, we investigate and compare the performance of several local and global smoothing techniques to a priori denoise the state measurements and numerically estimate the state time-derivatives to improve the accuracy and robustness of two sparse regression methods to recover governing equations: Sequentially Thresholded Least Squares (STLS) and Weighted Basis Pursuit Denoising (WBPDN) algorithms.
Sparse Identification of Nonlinear Dynamical Systems via Reweighted $\ell_1$-regularized Least Squares
The aim of this work is to improve the accuracy and robustness of SINDy in the presence of state measurement noise.
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