Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations

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dc.article.number116046
dc.catalogadorgjm
dc.contributor.authorTaç, Vahidullah
dc.contributor.authorRausch, Manuel K.
dc.contributor.authorSahli Costabal, Francisco
dc.contributor.authorTepole, Adrian Buganza
dc.date.accessioned2024-05-30T16:23:24Z
dc.date.available2024-05-30T16:23:24Z
dc.date.issued2023
dc.description.abstractWe develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy function and the dissipation potential with data-driven functions that a priori satisfy physics-based constraints such as objectivity and the second law of thermodynamics. Our approach enables modeling viscoelastic behavior of materials under arbitrary loads in three-dimensions even with large deformations and large deviations from the thermodynamic equilibrium. The data-driven nature of the governing potentials endows the model with much needed flexibility in modeling the viscoelastic behavior of a wide class of materials. We train the model using stress–strain data from biological and synthetic materials including human brain tissue, blood clots, natural rubber and human myocardium and show that the data-driven method outperforms traditional, closed-form models of viscoelasticity.
dc.fechaingreso.objetodigital2024-09-11
dc.format.extent22 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.cma.2023.116046
dc.identifier.urihttps://doi.org/10.1016/j.cma.2023.116046
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-85153044905&partnerID=MN8TOARS
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86070
dc.identifier.wosidWOS:000984851300001
dc.information.autorucEscuela de Ingeniería; Sahli Costabal, Francisco; S/I; 154857
dc.language.isoen
dc.nota.accesocontenido parcial
dc.revistaComputer Methods in Applied Mechanics and Engineering
dc.rightsacceso restringido
dc.subjectViscoelasticity
dc.subjectNeural ordinary differential equations
dc.subjectData-driven mechanics
dc.subjectTissue mechanics
dc.subjectNonlinear mechanics
dc.subjectPhysics-informed machine learning
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.titleData-driven anisotropic finite viscoelasticity using neural ordinary differential equations
dc.typeartículo
dc.volumen411
sipa.codpersvinculados154857
sipa.trazabilidadORCID;2024-05-27
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