Optimal influence cover for an element free Galerkin MFree method based on artificial neural network

The present investigation presents an efficient meshless method based on the weak form of an element-free-Galerkin method. The formulation of the numerical solution was conducted using an artificial neural network (ANN) approach to compute the optimal number of nodes in the influence domain for each...

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Main Authors: Hajjout, Imane, Haddouch, Manal, Boudi, El Mostapha
Format: Article
Language:English
Published: Académie des sciences 2020-03-01
Series:Comptes Rendus. Mécanique
Subjects:
Online Access:https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.5/
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author Hajjout, Imane
Haddouch, Manal
Boudi, El Mostapha
author_facet Hajjout, Imane
Haddouch, Manal
Boudi, El Mostapha
author_sort Hajjout, Imane
collection DOAJ
description The present investigation presents an efficient meshless method based on the weak form of an element-free-Galerkin method. The formulation of the numerical solution was conducted using an artificial neural network (ANN) approach to compute the optimal number of nodes in the influence domain for each point of interest. The numerical results using the ANN model were tested and compared with different approaches in the literature. Results show a reduction in the computational cost and an enhancement in an error criterion of up to 11%.
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spelling doaj.art-39fd8b70ea5a46c18e6e23ef5f8928432023-10-24T14:20:42ZengAcadémie des sciencesComptes Rendus. Mécanique1873-72342020-03-013481637610.5802/crmeca.510.5802/crmeca.5Optimal influence cover for an element free Galerkin MFree method based on artificial neural networkHajjout, Imanehttps://orcid.org/0000-0003-4131-8990Haddouch, ManalBoudi, El MostaphaThe present investigation presents an efficient meshless method based on the weak form of an element-free-Galerkin method. The formulation of the numerical solution was conducted using an artificial neural network (ANN) approach to compute the optimal number of nodes in the influence domain for each point of interest. The numerical results using the ANN model were tested and compared with different approaches in the literature. Results show a reduction in the computational cost and an enhancement in an error criterion of up to 11%.https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.5/Meshless methodsElement free GalerkinInfluence domainArtificial neural network
spellingShingle Hajjout, Imane
Haddouch, Manal
Boudi, El Mostapha
Optimal influence cover for an element free Galerkin MFree method based on artificial neural network
Comptes Rendus. Mécanique
Meshless methods
Element free Galerkin
Influence domain
Artificial neural network
title Optimal influence cover for an element free Galerkin MFree method based on artificial neural network
title_full Optimal influence cover for an element free Galerkin MFree method based on artificial neural network
title_fullStr Optimal influence cover for an element free Galerkin MFree method based on artificial neural network
title_full_unstemmed Optimal influence cover for an element free Galerkin MFree method based on artificial neural network
title_short Optimal influence cover for an element free Galerkin MFree method based on artificial neural network
title_sort optimal influence cover for an element free galerkin mfree method based on artificial neural network
topic Meshless methods
Element free Galerkin
Influence domain
Artificial neural network
url https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.5/
work_keys_str_mv AT hajjoutimane optimalinfluencecoverforanelementfreegalerkinmfreemethodbasedonartificialneuralnetwork
AT haddouchmanal optimalinfluencecoverforanelementfreegalerkinmfreemethodbasedonartificialneuralnetwork
AT boudielmostapha optimalinfluencecoverforanelementfreegalerkinmfreemethodbasedonartificialneuralnetwork