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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Format: | Article |
Language: | English |
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Elsevier
2018-12-01
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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 |
first_indexed | 2024-12-10T09:40:01Z |
format | Article |
id | doaj.art-fe13902fd9274f5898bc4d12922f9f2a |
institution | Directory Open Access Journal |
issn | 2095-0349 |
language | English |
last_indexed | 2024-12-10T09:40:01Z |
publishDate | 2018-12-01 |
publisher | Elsevier |
record_format | Article |
series | Theoretical and Applied Mechanics Letters |
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 |