99764 20230519145406.0 doi 10.5802/CRMECA.53 sideral 123302 ART-2021-123302 eng Ibanez, R. Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structures 2021 Access copy available to the general public Unrestricted The present work aims at analyzing issues related to the data manifold dimensionality. The interest of the study is twofold: (i) first, when too many measurable variables are considered, manifold learning is expected to extract useless variables; (ii) second, and more important, the same technique, manifold learning, could be utilized for identifying the necessity of employing latent extra variables able to recover single-valued outputs. Both aspects are discussed in the modeling of materials and structural systems by using unsupervised manifold learning strategies. info:eu-repo/grantAgreement/ES/UZ/ESI Group Chair info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 1.437 2021 MECHANICS 115 / 138 = 0.833 2021 Q4 T3 0.499 2021 Mechanics of Materials 2021 Q2 Materials Science (miscellaneous) 2021 Q2 3.8 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Gilormini, P. Cueto, E. Universidad de Zaragoza (orcid)0000-0003-1017-4381 Chinesta, F. 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 348, 10-11 (2021), 937-958 C. R. Mec. COMPTES RENDUS MECANIQUE 1631-0721 968540 http://zaguan.unizar.es/record/99764/files/texto_completo.pdf Versión publicada 847989 http://zaguan.unizar.es/record/99764/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:99764 articulos driver 2023-05-18-13:48:00 ARTICLE