PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data
arXiv:2004.08424
Abstract
PySINDy is a Python package for the discovery of governing dynamical systems models from data. In particular, PySINDy provides tools for applying the sparse identification of nonlinear dynamics (SINDy) (Brunton et al. 2016) approach to model discovery. In this work we provide a brief description of the mathematical underpinnings of SINDy, an overview and demonstration of the features implemented in PySINDy (with code examples), practical advice for users, and a list of potential extensions to PySINDy. Software is available at https://github.com/dynamicslab/pysindy.
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- Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
- Weak SINDy For Partial Differential Equations
- Physics-constrained, low-dimensional models for MHD: First-principles and data-driven approaches
- Applications and Techniques for Fast Machine Learning in Science