Data-driven computing in elasticity via kernel regression

Autor: Yoshihiro Kanno
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Theoretical and Applied Mechanics Letters, Vol 8, Iss 6, Pp 361-365 (2018)
Druh dokumentu: article
ISSN: 2095-0349
DOI: 10.1016/j.taml.2018.06.004
Popis: ABSTRACT: This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set. Keywords: Data-driven computational mechanics, Model-free method, Nonparametric method, Kernel regression, Nadaraya–Watson estimator
Databáze: Directory of Open Access Journals