Dynamical model identification via a method combining data driven and data assimilation approaches - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Dynamical model identification via a method combining data driven and data assimilation approaches

Résumé

Model-based control strategies require a dynamical model that is sufficiently accurate and robust with respect to the variation of the control parameters. When this model can not be determined using first principle equations, then identification techniques are needed. In this work, we present a general framework for identifying the parameters of a POD reduced-order model. The model obtained directly by POD Galerkin projection of the N-S equations is, in general, not robust. Here, we obtain a scalable identification of the parameters by a combined implementation of machine learning and data assimilation (DA) approaches. Recent advances in data driven techniques have given the possibility to learn the driving partial differential equations by using neural networks. However, without a partial knowledge of the underlying dynamics, the learning time may increase prohibitively with the number of parameters. To circumvent this difficulty, this work combines: i) PDE discovery methods to identify the parameters in the model, by using the physics-informed neural network 1 , and ii) Dual Ensemble Kalman filter 2 , a DA technique to correct both the predicted state and parameters.
Kumar_APSDFD2020_Abstract.pdf (52.91 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03088838 , version 1 (27-12-2020)

Identifiants

  • HAL Id : hal-03088838 , version 1

Citer

Nishant Kumar, Franck Kerhervé, Laurent Cordier. Dynamical model identification via a method combining data driven and data assimilation approaches. APS DFD 2020 virtual 73rd Annual Meeting of the APS Division of Fluid Dynamics, Nov 2020, Chicago, United States. ⟨hal-03088838⟩
34 Consultations
13 Téléchargements

Partager

Gmail Facebook X LinkedIn More