Hierarchical higher-order dynamic mode decomposition for clustering and feature selection-Reference-Cited by-同舟云学术

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Hierarchical higher-order dynamic mode decomposition for clustering and feature selection

Published:2024-03 Issue: Volume:158 Page:36-45
ISSN:0898-1221
Container-title:Computers & Mathematics with Applications
language:en
Short-container-title:Computers & Mathematics with Applications

Author:

Corrochano Adrián^ORCID,D'Alessio Giuseppe,Parente Alessandro,Le Clainche Soledad^ORCID

Funder

Universidad Politécnica de Madrid

European Research Council

Ministerio de Ciencia e Innovación

Fonds De La Recherche Scientifique - FNRS

Horizon 2020

Agencia Estatal de Investigación

Comunidad de Madrid

Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture

Publisher

Elsevier BV

Reference48 articles.

1. High-performance computing in computational fluid dynamics: progress and challenges;Cant;Philos. Trans. R. Soc. Lond. A, Math. Phys. Eng. Sci.,2002

2. Explicit reduced-order models for the stabilized finite element approximation of the incompressible Navier–Stokes equations;Baiges;Int. J. Numer. Methods Fluids,2013

3. Pod-Galerkin method for finite volume approximation of Navier–Stokes and rans equations;Lorenzi;Comput. Methods Appl. Mech. Eng.,2016

4. A reduced basis approach for pdes on parametrized geometries based on the shifted boundary finite element method and application to a Stokes flow;Karatzas;Comput. Methods Appl. Mech. Eng.,2019

5. Linear reduced-order model predictive control;Lorenzetti;IEEE Trans. Autom. Control,2022

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