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

Autor:	Ibanez, Ruben, Gilormini, Pierre, Cueto, Elias, Chinesta, Francisco
Jazyk:	English<br />French
Rok vydání:	2020
Předmět:	Nonsupervised manifold learning State variables Dimensionality reduction $k$-PCA Structural analysis Material constitutive equations Materials of engineering and construction. Mechanics of materials TA401-492
Zdroj:	Comptes Rendus. Mécanique, Vol 348, Iss 10-11, Pp 937-958 (2020)
Druh dokumentu:	article
ISSN:	1873-7234
DOI:	10.5802/crmeca.53
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/087c1dcc18cb4d0fbcb7824947f18c95 Zobrazit plný text záznamu View record in DOAJ