Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit

Reille, A. ; Hascoet, N. ; Ghnatios, C. ; Ammar, A. ; Cueto, E. (Universidad de Zaragoza) ; Duval, J.L. ; Chinesta, F. ; Keunings, R.

Resumen: The present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then they can be expressed very efficiently using few terms of a tensor decomposition. An efficient procedure is proposed as well as a way for extending it to nonlinear settings while keeping limited the impact of data noise. The proposed methodology is then validated by considering a nonlinear elastic problem and constructing the model relating tractions and displacements at the observation points.

Idioma: Inglés

DOI: 10.1016/j.crme.2019.11.003

Año: 2019

Publicado en: COMPTES RENDUS MECANIQUE 347, 11 (2019), 780-792
ISSN: 1631-0721

Factor impacto JCR: 1.509 (2019)

Categ. JCR: MECHANICS rank: 95 / 136 = 0.699 (2019) - Q3 - T3

Factor impacto SCIMAGO: 0.466 - Mechanics of Materials (Q2) - Materials Science (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T88
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R

Tipo y forma: Article (Published version)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)