PySINDy: A comprehensive Python package for robust sparse system identification
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Published version
Author(s)
Type
Journal Article
Abstract
Automated data-driven modeling, the process of directly discovering the governing equations
of a system from data, is increasingly being used across the scientific community. PySINDy
is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy,
we implement several advanced features that enable the discovery of more general differential
equations from noisy and limited data. The library of candidate terms is extended for the
identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling
techniques, are also implemented to improve performance for real-world data. Finally, we
provide a range of new optimization algorithms, including several sparse regression techniques
and algorithms to enforce and promote inequality constraints and stability. Together, these
updates enable entirely new SINDy model discovery capabilities that have not been reported
in the literature, such as constrained PDE identification and ensembling with different sparse
regression optimizers.
of a system from data, is increasingly being used across the scientific community. PySINDy
is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy,
we implement several advanced features that enable the discovery of more general differential
equations from noisy and limited data. The library of candidate terms is extended for the
identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling
techniques, are also implemented to improve performance for real-world data. Finally, we
provide a range of new optimization algorithms, including several sparse regression techniques
and algorithms to enforce and promote inequality constraints and stability. Together, these
updates enable entirely new SINDy model discovery capabilities that have not been reported
in the literature, such as constrained PDE identification and ensembling with different sparse
regression optimizers.
Date Issued
2022-01-29
Date Acceptance
2022-01-01
Citation
The Journal of Open Source Software, 2022, 7 (69), pp.1-7
ISSN
2475-9066
Publisher
Open Journals
Start Page
1
End Page
7
Journal / Book Title
The Journal of Open Source Software
Volume
7
Issue
69
Copyright Statement
© 2022 The Author(s). Authors of JOSS papers retain copyright.
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
License URL
Identifier
https://joss.theoj.org/papers/10.21105/joss.03994
Publication Status
Published
Date Publish Online
2022-01-29