doi:10.1016/j.crme.2019.11.003engReille, A.Hascoet, N.Ghnatios, C.Ammar, A.Cueto, E.Duval, J.L.Chinesta, F.Keunings, R.Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limitART-2019-122817The 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.2019http://zaguan.unizar.es/record/9930210.1016/j.crme.2019.11.003http://zaguan.unizar.es/record/99302oai:zaguan.unizar.es:99302info:eu-repo/grantAgreement/ES/DGA-FSE/T88info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-RCOMPTES RENDUS MECANIQUE 347, 11 (2019), 780-792by-nc-ndhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccess