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

Article dans une revue avec comité de lecture
Author
IBANEZ, Ruben
GILORMINI, Pierre
ccCUETO, Elias
95355 Universidad de Zaragoza = University of Zaragoza [Saragossa University] = Université de Saragosse
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/19941
DOI
10.5802/crmeca.53
Date
2020
Journal
Comptes Rendus Mécanique

Abstract

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.

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