doi:10.5802/CRMECA.53engIbanez, R.Gilormini, P.Cueto, E.Chinesta, F.Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structuresART-2021-123302The 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.2021http://zaguan.unizar.es/record/9976410.5802/CRMECA.53http://zaguan.unizar.es/record/99764oai:zaguan.unizar.es:99764info:eu-repo/grantAgreement/ES/UZ/ESI Group ChairCOMPTES RENDUS MECANIQUE 348, 10-11 (2021), 937-958byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess