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Bayesian model uncertainty quantification for hyperelastic soft tissue models

Zeraatpisheh, Milad, Bordas, Stephane P. A. ORCID: and Beex, Lars A. A. 2021. Bayesian model uncertainty quantification for hyperelastic soft tissue models. Data-Centric Engineering 2 , e9. 10.1017/dce.2021.9

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (,
Publisher: Cambridge University Press
ISSN: 2632-6736
Date of First Compliant Deposit: 28 October 2021
Date of Acceptance: 16 June 2021
Last Modified: 13 May 2023 03:03

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