Reference : Bayesian model uncertainty quantification for hyperelastic soft tissue models
Scientific journals : Article
Engineering, computing & technology : Mechanical engineering
Computational Sciences
http://hdl.handle.net/10993/47818
Bayesian model uncertainty quantification for hyperelastic soft tissue models
English
Zeraatpisheh, Milad mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Beex, Lars mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Bordas, Stéphane mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
13-Jul-2021
Data-Centric Engineering
Cambridge University Press
Yes
International
[en] Model uncertainty ; Bayesian inference ; incompressible hyperelasticity ; soft tissues
[en] Patient-specific surgical simulations require the patient-specific identification of the constitutive parameters. The sparsity of the experimental data and the substantial noise in the data (e.g., recovered during surgery) cause considerable uncertainty in the identification. In this exploratory work, parameter uncertainty for incompressible hyperelasticity, often used for soft tissues, is addressed by a probabilistic identification approach based on Bayesian inference. Our study particularly focuses on the uncertainty of the model: we investigate how the identified uncertainties of the constitutive parameters behave when different forms of model uncertainty are considered. The model uncertainty formulations range from uninformative ones to more accurate ones that incorporate more detailed extensions of incompressible hyperelasticity. The study shows that incorporating model uncertainty may improve the results, but this is not guaranteed.
The project is funded by European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No.764644.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/47818
also: http://hdl.handle.net/10993/47878
10.1017/dce.2021.9

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