A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids

Abstract We propose and implement a computational procedure to establish data-driven surrogate constitutive models for heterogeneous materials. We study the multiaxial response of non-linear n-phase composites via Finite Element (FE) simulations and computational homogenisation. Pseudo-random, multi...

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Autores principales:	Weijian Ge, Vito L. Tagarielli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8c6f3d270e9143ca9b35312983a993bd
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spelling	oai:doaj.org-article:8c6f3d270e9143ca9b35312983a993bd2021-12-02T18:49:28ZA computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids10.1038/s41598-021-94957-02045-2322https://doaj.org/article/8c6f3d270e9143ca9b35312983a993bd2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94957-0https://doaj.org/toc/2045-2322Abstract We propose and implement a computational procedure to establish data-driven surrogate constitutive models for heterogeneous materials. We study the multiaxial response of non-linear n-phase composites via Finite Element (FE) simulations and computational homogenisation. Pseudo-random, multiaxial, non-proportional histories of macroscopic strain are imposed on volume elements of n-phase composites, subject to periodic boundary conditions, and the corresponding histories of macroscopic stresses and plastically dissipated energy are recorded. The recorded data is used to train surrogate, phenomenological constitutive models based on neural networks (NNs), and the accuracy of these models is assessed and discussed. We analyse heterogeneous composites with hyperelastic, viscoelastic or elastic–plastic local constitutive descriptions. In each of these three cases, we propose and assess optimal choices of inputs and outputs for the surrogate models and strategies for their training. We find that the proposed computational procedure can capture accurately and effectively the response of non-linear n-phase composites subject to arbitrary mechanical loading.Weijian GeVito L. TagarielliNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Weijian Ge Vito L. Tagarielli A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids
description	Abstract We propose and implement a computational procedure to establish data-driven surrogate constitutive models for heterogeneous materials. We study the multiaxial response of non-linear n-phase composites via Finite Element (FE) simulations and computational homogenisation. Pseudo-random, multiaxial, non-proportional histories of macroscopic strain are imposed on volume elements of n-phase composites, subject to periodic boundary conditions, and the corresponding histories of macroscopic stresses and plastically dissipated energy are recorded. The recorded data is used to train surrogate, phenomenological constitutive models based on neural networks (NNs), and the accuracy of these models is assessed and discussed. We analyse heterogeneous composites with hyperelastic, viscoelastic or elastic–plastic local constitutive descriptions. In each of these three cases, we propose and assess optimal choices of inputs and outputs for the surrogate models and strategies for their training. We find that the proposed computational procedure can capture accurately and effectively the response of non-linear n-phase composites subject to arbitrary mechanical loading.
format	article
author	Weijian Ge Vito L. Tagarielli
author_facet	Weijian Ge Vito L. Tagarielli
author_sort	Weijian Ge
title	A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids
title_short	A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids
title_full	A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids
title_fullStr	A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids
title_full_unstemmed	A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids
title_sort	computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8c6f3d270e9143ca9b35312983a993bd
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A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids

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