Generative hyperelasticity with physics-informed probabilistic diffusion fields

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dc.contributor.authorTac, Vahidullah
dc.contributor.authorRausch, Manuel K.
dc.contributor.authorBilionis, Ilias
dc.contributor.authorSahli Costabal, Francisco
dc.contributor.authorTepole, Adrian Buganza
dc.date.accessioned2025-01-20T16:17:37Z
dc.date.available2025-01-20T16:17:37Z
dc.date.issued2024
dc.description.abstractMany natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy functions possess the flexibility to capture the behavior of these complex materials with high accuracy while satisfying physics-based constraints. However, most of these approaches disregard the uncertainty in the estimates and the spatial heterogeneity of these materials. In this work, we leverage recent advances in generative models to address these issues. We use as building block neural ordinary equations (NODE) that-by construction-create polyconvex strain energy functions, a key property of realistic hyperelastic material models. We combine this approach with probabilistic diffusion models to generate new samples of strain energy functions. This technique allows us to sample a vector of Gaussian white noise and translate it to NODE parameters thereby representing plausible strain energy functions. We extend our approach to spatially correlated diffusion resulting in heterogeneous material properties for arbitrary geometries. We extensively test our method with synthetic and experimental data on biological tissues and run finite element simulations with various degrees of spatial heterogeneity. We believe this approach is a major step forward including uncertainty in predictive, data-driven models of hyperelasticity.
dc.fuente.origenWOS
dc.identifier.doi10.1007/s00366-024-01984-2
dc.identifier.eissn1435-5663
dc.identifier.issn0177-0667
dc.identifier.urihttps://doi.org/10.1007/s00366-024-01984-2
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90606
dc.identifier.wosidWOS:001226809000001
dc.language.isoen
dc.revistaEngineering with computers
dc.rightsacceso restringido
dc.subjectHyperelasticity
dc.subjectGenerative modeling
dc.subjectNeural ODEs
dc.subjectData-driven modeling
dc.subjectHeterogeneous materials
dc.subjectPolyconvex
dc.subjectHyper-network
dc.titleGenerative hyperelasticity with physics-informed probabilistic diffusion fields
dc.typeartículo
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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