Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures

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dc.identifier.uri http://dx.doi.org/10.15488/4769
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/4811
dc.contributor.author Hamdia, Khader M. ger
dc.contributor.author Ghasemi, Hamid ger
dc.contributor.author Zhuang, Xiaoying ger
dc.contributor.author Alajlan, Naif ger
dc.contributor.author Rabczuk, Timon ger
dc.date.accessioned 2019-04-30T06:53:48Z
dc.date.available 2019-04-30T06:53:48Z
dc.date.issued 2019
dc.identifier.citation Hamdia, K.M.; Ghasemi, H.; Zhuang, X.; Alajlan, N.; Rabczuk, T.: Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures. In: Computers, Materials & Continua 59 (2019), Nr. 1, S. 79-87. DOI: https://10.32604/cmc.2019.05882. ger
dc.description.abstract In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A Non-Uniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the strain in the stress direction and the electric potential due flexoelectricity. The dataset are generated from the forward analysis of the flexoelectric model. 80% of the dataset is used for training purpose while the remaining is used for validation by checking the mean squared error. In addition to the input and output layers, the developed DNN model is composed of four hidden layers. The results showed high predictions capabilities of the proposed method with much lower computational time in comparison to the numerical model. ger
dc.language.iso eng ger
dc.publisher Henderson : Tech Science Press
dc.relation.ispartofseries Computers, Materials & Continua 59 (2019), Nr. 1 ger
dc.rights CC BY 4.0 ger
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Flexoelectricity eng
dc.subject Isogeometric analysis eng
dc.subject Machine learning prediction eng
dc.subject deep neural networks eng
dc.subject.ddc 004 | Informatik ger
dc.title Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures ger
dc.type article ger
dc.type Text ger
dc.relation.essn 1546-2218
dc.relation.issn 1546-2226
dc.relation.doi 10.32604/cmc.2019.05882
dc.description.version publishedVersion ger
tib.accessRights frei zug�nglich


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