Repository for the paper A unified sparse optimization framework to learn parsimonious physics-informed models from data by Kathleen Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, and J. Nathan Kutz.
This work implements the sparse regularized relaxed regression (SR3) optimization method for sparse identification of nonlinear dynamics (SINDy), including a number of extensions to the algorithm for corrupt data trimming, incorporating physical constraints, and fitting parameterized library functions. Code examples can be found as jupyter notebooks in the examples directory.
This code requires the installation of the PySINDy package.