PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Open Source Software Année : 2020

PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data

Résumé

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 models in particular have been widely used to study, explain, and predict system behavior in a wide range of application areas, with examples ranging from Newton’s laws of classical mechanics to the Michaelis-Menten kinetics for modeling enzyme kinetics. While governing laws and equations were traditionally derived by hand, the current growth of available measurement data and resulting emphasis on data-driven modeling motivates algorithmic approaches for model discovery. A number of such approaches have been developed in recent years and have generated widespread interest, including Eureqa (Schmidt & Lipson, 2009), sure independence screening and sparsifying operator (Ouyang, Curtarolo, Ahmetcik, Scheffler, & Ghiringhelli, 2018), and the sparse identification of nonlinear dynamics (SINDy) (Brunton, Proctor, & Kutz, 2016). Maximizing the impact of these model discovery methods requires tools to make them widely accessible to scientists across domains and at various levels of mathematical expertise.
Fichier principal
Vignette du fichier
DynFluid_JOSS_2020_LOISEAU.pdf (72.23 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02648569 , version 1 (29-05-2020)

Identifiants

Citer

Brian de Silva, Kathleen Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, et al.. PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data. Journal of Open Source Software, 2020, 5 (49), pp.1-4. ⟨10.21105/joss.02104⟩. ⟨hal-02648569⟩
349 Consultations
312 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More