Physics-informed neural networks for solving elasticity problems

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dc.contributor.authorAlmeida, Estevão Fuzaro [UNESP]
dc.contributor.authorSilva, Samuel da [UNESP]
dc.contributor.authorCunha Júnior, Americo
dc.contributor.institutionSão Paulo Research Foundation (FAPESP) Grant No 2022/16156-9
dc.date.accessioned2024-03-11T19:09:18Z
dc.date.available2024-03-11T19:09:18Z
dc.date.issued2023-12-08
dc.descriptionThe first author would like to thank São Paulo Research Foundation (FAPESP) for providing financial support under grant number 2022/16156-9.
dc.description.abstractComputational mechanics has seen remarkable progress in recent years due to the integration of machine learning techniques, particularly neural networks. Traditional approaches in solid mechanics, such as the finite element method (FEM), often require extensive manual labor in discretization and mesh generation, making them time-consuming and challenging for complex geometries. Moreover, these methods heavily rely on accurate and complete data, which may not always be readily available or prone to measurement errors. On the other hand, Physics-Informed Neural Networks (PINNs) are a machine learning technique that can learn from data and physics equations, allowing accurate and physically consistent predictions. Through this study, we aim to demonstrate the effectiveness of PINNs in accurately predicting the stress distribution in a triangular plate, showcasing their potential as a valuable tool in solving real-world solid mechanics problems. Combining the elasticity conservation laws and boundary conditions into the neural network architecture creates a PINN and is trained on a coarse mesh of points over the plate domain and evaluated on a fine mesh using a data-free approach, compared with the Airy analytical solution.en
dc.identifier.citationALMEIDA, E. F.; SILVA, S.; CUNHA JR, A. Physics-informed neural networks for solving elasticity problems. In: BRAZILIAN CONGRESS OF THERMAL SCIENCES AND ENGINEERING, 27th, 2023, Florianópolis. Proceedings [...] Rio de Janeiro: ABCM, 2023. v. 1. p. 1-8.
dc.identifier.doi10.26678/ABCM.COBEM2023.COB2023-0310
dc.identifier.lattes1577918465935468
dc.identifier.lattes6807553800607803
dc.identifier.orcid0000-0001-7406-8698
dc.identifier.orcid0000-0001-6430-3746
dc.identifier.urihttps://hdl.handle.net/11449/253634
dc.language.isoeng
dc.publisherAssociação Brasileira de Engenharia e Ciências Mecânicas (ABCM)
dc.rights.accessRightsAcesso aberto
dc.subjectSolid mechanicsen
dc.subjectPhysics-informed neural networksen
dc.subjectStress distributionen
dc.subjectData-free modelingen
dc.titlePhysics-informed neural networks for solving elasticity problems
dc.typeTrabalho apresentado em evento
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Ilha Solteira
unesp.departmentEngenharia Mecânica - FEISpt

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Ilha Solteira - FEIS - Faculdade de Engenharia