Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold

Data-driven computing in elasticity attempts to directly use experimental data on material, without constructing an empirical model of the constitutive relation, to predict an equilibrium state of a structure subjected to a specified external load. Provided that a data set comprising stress–strain p...

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Autor principal:	Yoshihiro Kanno
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Alternating minimization Regularized least-squares Kernel method Manifold learning Data-driven computing Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/7811f04a4abb47539f5b12f02939f59b
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id	oai:doaj.org-article:7811f04a4abb47539f5b12f02939f59b
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spelling	oai:doaj.org-article:7811f04a4abb47539f5b12f02939f59b2021-11-30T04:15:41ZAlternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold2095-034910.1016/j.taml.2021.100289https://doaj.org/article/7811f04a4abb47539f5b12f02939f59b2021-07-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2095034921000969https://doaj.org/toc/2095-0349Data-driven computing in elasticity attempts to directly use experimental data on material, without constructing an empirical model of the constitutive relation, to predict an equilibrium state of a structure subjected to a specified external load. Provided that a data set comprising stress–strain pairs of material is available, a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie (Kanno 2021, Jpn. J. Ind. Appl. Math.). From the perspective of physical experiments, stress field cannot be directly measured, while displacement and force fields are measurable. In this study, we extend the previous kernel method to the situation that pairs of displacement and force, instead of pairs of stress and strain, are available as an input data set. A new regularized least-squares problem is formulated in this problem setting, and an alternating minimization algorithm is proposed to solve the problem.Yoshihiro KannoElsevierarticleAlternating minimizationRegularized least-squaresKernel methodManifold learningData-driven computingEngineering (General). Civil engineering (General)TA1-2040ENTheoretical and Applied Mechanics Letters, Vol 11, Iss 5, Pp 100289- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Alternating minimization Regularized least-squares Kernel method Manifold learning Data-driven computing Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Alternating minimization Regularized least-squares Kernel method Manifold learning Data-driven computing Engineering (General). Civil engineering (General) TA1-2040 Yoshihiro Kanno Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold
description	Data-driven computing in elasticity attempts to directly use experimental data on material, without constructing an empirical model of the constitutive relation, to predict an equilibrium state of a structure subjected to a specified external load. Provided that a data set comprising stress–strain pairs of material is available, a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie (Kanno 2021, Jpn. J. Ind. Appl. Math.). From the perspective of physical experiments, stress field cannot be directly measured, while displacement and force fields are measurable. In this study, we extend the previous kernel method to the situation that pairs of displacement and force, instead of pairs of stress and strain, are available as an input data set. A new regularized least-squares problem is formulated in this problem setting, and an alternating minimization algorithm is proposed to solve the problem.
format	article
author	Yoshihiro Kanno
author_facet	Yoshihiro Kanno
author_sort	Yoshihiro Kanno
title	Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold
title_short	Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold
title_full	Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold
title_fullStr	Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold
title_full_unstemmed	Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold
title_sort	alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7811f04a4abb47539f5b12f02939f59b
work_keys_str_mv	AT yoshihirokanno alternatingminimizationfordatadrivencomputationalelasticityfromexperimentaldatakernelmethodforlearningconstitutivemanifold
_version_	1718406811817607168

Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold

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