99302 20210223204725.0 doi 10.1016/j.crme.2019.11.003 sideral 122817 ART-2019-122817 eng Reille, A. Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit 2019 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/DGA-FSE/T88 info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 1.509 2019 MECHANICS 95 / 136 = 0.699 2019 Q3 T3 0.466 2019 Mechanics of Materials 2019 Q2 Materials Science (miscellaneous) 2019 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Hascoet, N. Ghnatios, C. Ammar, A. (orcid)0000-0003-1017-4381 Cueto, E. Universidad de Zaragoza Duval, J.L. Chinesta, F. Keunings, R. 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 347, 11 (2019), 780-792 C. R. Mec. COMPTES RENDUS MECANIQUE 1631-0721 439107 http://zaguan.unizar.es/record/99302/files/texto_completo.pdf Versión publicada 1937875 http://zaguan.unizar.es/record/99302/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:99302 articulos driver 2021-02-23-19:03:14 ARTICLE