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

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arxiv 2004.08424 v1 pith:3QXPF2SL submitted 2020-04-17 math.DS physics.comp-ph

Brian M. de Silva , Kathleen Champion , Markus Quade , Jean-Christophe Loiseau , J. Nathan Kutz , Steven L. Brunton This is my paper · ORCID
classification math.DSphysics.comp-ph
keywords pysindydatadiscoverydynamicsidentificationnonlinearpackagepython
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Data-driven discovery of dynamo cycle equations

    astro-ph.SR 2026-03 conditional novelty 6.5 of 10

    SINDy on Hankel-DMD coefficients recovers supercritical and subcritical dynamo normal forms that extrapolate better than weakly nonlinear analysis, including unstable branches and non-analytic nonlinearities.

  2. Discovering Interpretable Ordinary Differential Equations from Noisy Data

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A pipeline that fits noisy data with a smooth general-solution function, differentiates it with B-splines, and uses SVD to recover a constant-coefficient linear ODE.

  3. Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks

    cs.LG 2025-05 conditional novelty 5.0 of 10

    This paper introduces a physics-informed neural network that uses Deep Sets to encode sensor data, allowing one model to adapt to new parameters and boundary conditions without retraining, with tests on a chaotic ODE,...

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