PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data
Article dans une revue avec comité de lecture
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.
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Article dans une revue avec comité de lectureKAPTANOGLU, Alan; DE SILVA, Brian; FASEL, Urban; KAHEMAN, Kadierdan; GOLDSCHMIDT, Andy; CALLAHAM, Jared; DELAHUNT, Charles; NICOLAOU, Zachary; CHAMPION, Kathleen; KUTZ, J.; BRUNTON, Steven; LOISEAU, Jean-Christophe (The Open Journal, 2022-01)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 ...
Chapitre d'ouvrage scientifiqueReduced-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 ...
Article dans une revue avec comité de lectureCALLAHAM, Jared L.; BRUNTON, Steven L.; LOISEAU, 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 ...
Article dans une revue avec comité de lectureLOISEAU, Jean-Christophe; BRUNTON, Steven L. (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 ...
Article dans une revue avec comité de lectureLOISEAU, Jean-Christophe; NOACK, Bernd R.; BRUNTON, Steven L. (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 ...