New trends in computational mechanics: model order reduction, manifold learning and data-driven - Institut de Recherche en Génie Civil et Mécanique Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

New trends in computational mechanics: model order reduction, manifold learning and data-driven

Résumé

Engineering sciences and technology is experiencing the data revolution. In the past models were more abundant than data, too expensive to be collected and analyzed at that time. However, nowadays, the situation is radically different, data is much more abundant (and accurate sometimes) than existing models, and a new paradigm is emerging in engineering sciences and technology. This paper retraces some incipient applications based on data within the framework of computational mechanics. Three main topics are addressed in the present work: (i) construction of solution manifolds and its use for interpolating new solutions on the man-ifold; (ii) constructing parametric solutions on the just extracted manifold; and (iii) defining behavior manifolds to perform data-driven simulation while avoiding the use of usual constitu-tive equations.
Fichier principal
Vignette du fichier
ICACM2016.pdf (244.87 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01878190 , version 1 (20-09-2018)

Identifiants

  • HAL Id : hal-01878190 , version 1

Citer

Jose Vicente Aguado, Domenico Borzacchiello, Elena Lopez, Emmanuelle Abisset-Chavanne, David González, et al.. New trends in computational mechanics: model order reduction, manifold learning and data-driven. 9th Annual US-France symposium of the International Center for Applied Computational Mechanics, Jun 2016, Compiègne, France. ⟨hal-01878190⟩
224 Consultations
305 Téléchargements

Partager

Gmail Facebook X LinkedIn More