Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structures

Ibanez, R. ; Gilormini, P. ; Cueto, E. (Universidad de Zaragoza) ; Chinesta, F.

Resumen: 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.

Idioma: Inglés

DOI: 10.5802/CRMECA.53

Año: 2021

Publicado en: COMPTES RENDUS MECANIQUE 348, 10-11 (2021), 937-958
ISSN: 1631-0721

Factor impacto JCR: 1.437 (2021)

Categ. JCR: MECHANICS rank: 115 / 138 = 0.833 (2021) - Q4 - T3

Factor impacto CITESCORE: 3.8 - Materials Science (Q2) - Engineering (Q2)

Factor impacto SCIMAGO: 0.499 - Mechanics of Materials (Q2) - Materials Science (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/ES/UZ/ESI Group Chair

Tipo y forma: Article (Published version)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)