no code implementations • 29 Jan 2022 • Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan
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.
no code implementations • 27 May 2020 • Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan
The aim of this work is to improve the accuracy and robustness of SINDy in the presence of state measurement noise.