PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data
TypeArticles dans des revues 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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LOISEAU, Jean-Christophe; BRUNTON, Steven L. (Cambridge University Press, 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 ...
LOISEAU, Jean-Christophe; NOACK, Bernd R.; BRUNTON, Steven L. (Cambridge University Press, 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 ...
PICELLA, Francesco; LOISEAU, Jean-Christophe; LUSSEYRAN, F; ROBINET, Jean-Christophe; CHERUBINI, Stefania; PASTUR, L (Cambridge University Press, 2018)The transition to unsteadiness of a three-dimensional open cavity flow is investigated using the joint application of direct numerical simulations and fully three-dimensional linear stability analyses, providing a clear ...
Investigation of the roughness-induced transition: global stability analyses and direct numerical simulations LOISEAU, Jean-Christophe; ROBINET, Jean-Christophe; CHERUBINI, Stefania; LERICHE, Emmanuel (Cambridge University Press, 2014)The linear global instability and resulting transition to turbulence induced by an isolated cylindrical roughness element of height h and diameter d immersed within an incompressible boundary layer flow along a flat plate ...
BUCCI, Michele Alessandro; PUCKERT, D. K.; ANDRIANO, C.; LOISEAU, Jean-Christophe; CHERUBINI, Stefania; ROBINET, Jean-Christophe; RIST, U. (Cambridge University Press, 2017)The onset of unsteadiness in a boundary-layer flow past a cylindrical roughness element is investigated for three flow configurations at subcritical Reynolds numbers, both experimentally and numerically. On the one hand, ...