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PySINDy: A comprehensive Python package for robust sparse system identification

Article dans une revue avec comité de lecture
Auteur
KAPTANOGLU, Alan
DE SILVA, Brian
300433 University of Washington [Seattle]
FASEL, Urban
545712 Department of Mechanical Engineering [University of Washington]
KAHEMAN, Kadierdan
545712 Department of Mechanical Engineering [University of Washington]
GOLDSCHMIDT, Andy
300433 University of Washington [Seattle]
CALLAHAM, Jared
545712 Department of Mechanical Engineering [University of Washington]
DELAHUNT, Charles
300433 University of Washington [Seattle]
NICOLAOU, Zachary
300433 University of Washington [Seattle]
CHAMPION, Kathleen
300433 University of Washington [Seattle]
KUTZ, J.
300433 University of Washington [Seattle]
BRUNTON, Steven
300433 University of Washington [Seattle]
545712 Department of Mechanical Engineering [University of Washington]
ccLOISEAU, Jean-Christophe
134975 Laboratoire de Dynamique des Fluides [DynFluid]

URI
http://hdl.handle.net/10985/23054
DOI
10.21105/joss.03994
Date
2022-01
Journal
Journal of Open Source Software

Résumé

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.

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PySINDy: A comprehensive Python ...
Fin d'embargo:
2022-07-30
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Cette publication figure dans le(s) laboratoire(s) suivant(s)

  • Dynamique des Fluides (DynFluid)

Documents liés

Visualiser des documents liés par titre, auteur, créateur et sujet.

  • PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data 
    Article dans une revue avec comité de lecture
    DE SILVA, Brian; CHAMPION, Kathleen; QUADE, Markus; KUTZ, J. Nathan; BRUNTON, Steven; ccLOISEAU, Jean-Christophe (Open Journals, 2020)
    Scientists have long quantified empirical observations by developing mathematical models that characterize the observations, have some measure of interpretability, and are capable of making predictions. Dynamical systems ...
  • From the POD-Galerkin method to sparse manifold models 
    Chapitre d'ouvrage scientifique
    BRUNTON, Steven; NOACK, Bernd; ccLOISEAU, Jean-Christophe (De Gruyter, 2021-06)
    Reduced-order models are essential for the accurate and efficient prediction, estimation, and control of complex systems. This is especially true in fluid dynamics, where the fully resolved state space may easily contain ...
  • On the role of nonlinear correlations in reduced-order modelling 
    Article dans une revue avec comité de lecture
    CALLAHAM, Jared L.; BRUNTON, Steven L.; ccLOISEAU, Jean-Christophe (Cambridge University Press (CUP), 2022-03)
    This work investigates nonlinear dimensionality reduction as a means of improving the accuracy and stability of reduced-order models of advection-dominated flows. Nonlinear correlations between temporal proper orthogonal ...
  • Sparse reduced-order modelling: sensor-based dynamics to full-state estimation 
    Article dans une revue avec comité de lecture
    NOACK, Bernd R.; BRUNTON, Steven L.; ccLOISEAU, Jean-Christophe (Cambridge University Press (CUP), 2018)
    We propose a general dynamic reduced-order modelling framework for typical experimental data: time-resolved sensor data and optional non-time-resolved particle image velocimetry (PIV) snapshots. This framework can be ...
  • Constrained sparse Galerkin regression 
    Article dans une revue avec comité de lecture
    BRUNTON, Steven L.; ccLOISEAU, Jean-Christophe (Cambridge University Press (CUP), 2018)
    The sparse identification of nonlinear dynamics (SINDy) is a recently proposed data-driven modelling framework that uses sparse regression techniques to identify nonlinear low-order models. With the goal of low-order models ...

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