A deep learning method for solving thermoelastic coupling problem.
The study of thermoelasticity problems holds significant importance in the field of engineering. When analyzing non-Fourier thermoelastic problems, it was found that as the thermal relaxation time increases, the finite element solution will face convergence difficulties. Therefore, it is necessary t...
| Publicado en: | Zeitschrift für Naturforschung Section A: A Journal of Physical Sciences Vol. 79; no. 8; pp. 851 - 872 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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De Gruyter
Aug2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178815637&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178815637 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 09320784 FL07 jtl: Zeitschrift für Naturforschung Section A: A Journal of Physical Sciences issn: 09320784 maglogo: N pubinfo: dt: Aug2024 vid: 79 iid: 8 pid: 1734 pub: De Gruyter artinfo: ui: 178815637 10.1515/zna-2024-0009 ppf: 851 ppct: 21 formats: tig: atl: A deep learning method for solving thermoelastic coupling problem. aug: au: Fang, Ruoshi Zhang, Kai Song, Ke Kai, Yue Li, Yong Zheng, Bailin affil: School of Aerospace Engineering and Applied Mechanics, 12476 Tongji University School of Automotive Studies, 12476 Tongji University, No. 4800, Cao'an Road, Shanghai, 201804, China School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, No. 333, Longteng Road, Shanghai, 201620, China School of Intelligent Manufacturing and Control Engineering, 74598 Shanghai Polytechnic University, No. 2360, Jinhai Road, Shanghai, 201209, China su: Thermal stresses Thermoelasticity Engineering Deep learning Equations sug: subj: Thermal stresses Thermoelasticity Engineering Deep learning Equations keyword: deep learning physics-inspired neural network thermal stress thermoelastic coupling ab: The study of thermoelasticity problems holds significant importance in the field of engineering. When analyzing non-Fourier thermoelastic problems, it was found that as the thermal relaxation time increases, the finite element solution will face convergence difficulties. Therefore, it is necessary to use alternative methods to solve. This paper proposes a physics-informed neural network (PINN) based on the DeepXDE deep learning library to analyze thermoelastic problems, including classical thermoelastic problems, thermoelastic coupling problems, and generalized thermoelastic problems. The loss function is constructed based on equations, initial conditions, and boundary conditions. Unlike traditional data-driven methods, this approach does not rely on known solutions. By comparing with analytical and finite element solutions, the applicability and accuracy of the deep learning method have been validated, providing new insights for the study of thermoelastic problems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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