Data-driven computing in elasticity via kernel regression

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 num...

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Main Author: Yoshihiro Kanno
Format: Article
Language:English
Published: Elsevier 2018-12-01
Series:Theoretical and Applied Mechanics Letters
Online Access:http://www.sciencedirect.com/science/article/pii/S2095034918302071
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author Yoshihiro Kanno
author_facet Yoshihiro Kanno
author_sort Yoshihiro Kanno
collection DOAJ
description 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
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spelling doaj.art-fe13902fd9274f5898bc4d12922f9f2a2022-12-22T01:54:02ZengElsevierTheoretical and Applied Mechanics Letters2095-03492018-12-0186361365Data-driven computing in elasticity via kernel regressionYoshihiro Kanno0Corresponding author; Mathematics and Informatics Center, The University of Tokyo, Tokyo 113-8656, JapanABSTRACT: 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 estimatorhttp://www.sciencedirect.com/science/article/pii/S2095034918302071
spellingShingle Yoshihiro Kanno
Data-driven computing in elasticity via kernel regression
Theoretical and Applied Mechanics Letters
title Data-driven computing in elasticity via kernel regression
title_full Data-driven computing in elasticity via kernel regression
title_fullStr Data-driven computing in elasticity via kernel regression
title_full_unstemmed Data-driven computing in elasticity via kernel regression
title_short Data-driven computing in elasticity via kernel regression
title_sort data driven computing in elasticity via kernel regression
url http://www.sciencedirect.com/science/article/pii/S2095034918302071
work_keys_str_mv AT yoshihirokanno datadrivencomputinginelasticityviakernelregression