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)