PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data

TL;DR

PySINDy is a Python package that simplifies the discovery of nonlinear dynamical systems models from data using the sparse identification of nonlinear dynamics (SINDy) method, offering tools, examples, and guidance for researchers.

Contribution

This work introduces PySINDy, a comprehensive Python package that implements SINDy for model discovery, with user-friendly features and practical advice for application.

Findings

Provides a Python implementation of SINDy for data-driven model discovery

Includes code examples and practical guidance for users

Enables efficient identification of nonlinear dynamical systems from data

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.