Data-driven tissue mechanics with polyconvex neural ordinary differential equations

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dc.article.number115248
dc.catalogadorgjm
dc.contributor.authorTaç, Vahidullah
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.issued2022
dc.description.abstractData-driven methods are becoming an essential part of computational mechanics due to their advantages over traditional material modeling. Deep neural networks are able to learn complex material response without the constraints of closed-form models. However, data-driven approaches do not a priori satisfy physics-based mathematical requirements such as polyconvexity, a condition needed for the existence of minimizers for boundary value problems in elasticity. In this study, we use a recent class of neural networks, neural ordinary differential equations (N-ODEs), to develop data-driven material models that automatically satisfy polyconvexity of the strain energy. We take advantage of the properties of ordinary differential equations to create monotonic functions that approximate the derivatives of the strain energy with respect to deformation invariants. The monotonicity of the derivatives guarantees the convexity of the energy. The N-ODE material model is able to capture synthetic data generated from closed-form material models, and it outperforms conventional models when tested against experimental data on skin, a highly nonlinear and anisotropic material. We also showcase the use of the N-ODE material model in finite element simulations of reconstructive surgery. The framework is general and can be used to model a large class of materials, especially biological soft tissues. We therefore expect our methodology to further enable data-driven methods in computational mechanics.
dc.format.extent18 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.cma.2022.115248
dc.identifier.urihttps://doi.org/10.1016/j.cma.2022.115248
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-85134352266&partnerID=MN8TOARS
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86074
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.subjectMachine learning
dc.subjectConstitutive modeling
dc.subjectNonlinear finite elements
dc.subjectSkin mechanics
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.titleData-driven tissue mechanics with polyconvex neural ordinary differential equations
dc.typeartículo
dc.volumen398
sipa.codpersvinculados154857
sipa.trazabilidadORCID;2024-05-27
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