Thermoelastic Analysis of Functionally Graded Spherical Bodies Using Deep Neural Networks

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dc.contributor.authorGönczi, Dávid
dc.date.accessioned2026-01-15T09:26:28Z
dc.date.available2026-01-15T09:26:28Z
dc.date.issued2025-10-18
dc.description.abstractThis paper deals with the numerical analysis of functionally graded spherical bodies subjected to combined thermal and mechanical loads. A method is presented to train deep neural networks to approximate the important solutions. We outline two approaches for generating the training dataset for a deep neural network, followed by a method for creating the neural network itself. Then, through a numerical example, we investigate the axisymmetric problems of radially graded spherical bodies (e.g., ideal spherical pressure vessels). Based on the results obtained, we evaluate the accuracy of solving the outlined problem using the proposed neural network.en
dc.description.abstractThis paper deals with the numerical analysis of functionally graded spherical bodies subjected to combined thermal and mechanical loads. A method is presented to train deep neural networks to approximate the important solutions. We outline two approaches for generating the training dataset for a deep neural network, followed by a method for creating the neural network itself. Then, through a numerical example, we investigate the axisymmetric problems of radially graded spherical bodies (e.g., ideal spherical pressure vessels). Based on the results obtained, we evaluate the accuracy of solving the outlined problem using the proposed neural network.hu
dc.formatapplication/pdf
dc.identifier.citationInternational Journal of Engineering and Management Sciences, Vol. 10 No. 4 (2025) , 57-66
dc.identifier.doihttps://doi.org/10.21791/IJEMS.2025.19
dc.identifier.eissn2498-700X
dc.identifier.issue4
dc.identifier.jtitleInternational Journal of Engineering and Management Sciences
dc.identifier.urihttps://hdl.handle.net/2437/402479
dc.identifier.volume10
dc.languageen
dc.relationhttps://ojs.lib.unideb.hu/IJEMS/article/view/16083
dc.rights.accessOpen Access
dc.rights.ownerDávid Gönczi
dc.subjectFGM Sphereen
dc.subjectNeural Networksen
dc.subjectFEMen
dc.subjectThermoelasticityen
dc.subjectFGM Spherehu
dc.subjectNeural Networkshu
dc.subjectFEMhu
dc.subjectThermoelasticityhu
dc.titleThermoelastic Analysis of Functionally Graded Spherical Bodies Using Deep Neural Networksen
dc.typefolyóiratcikkhu
dc.typearticleen
dc.type.detailedidegen nyelvű folyóiratközlemény hazai lapbanhu
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International Journal of Engineering and Management Sciences (DE-journals)