Bayesian model uncertainty quantification for hyperelastic soft tissue models
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 wor...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2021-01-01
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| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673621000095/type/journal_article |
| _version_ | 1811156422725992448 |
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| author | Milad Zeraatpisheh Stephane P.A. Bordas Lars A.A. Beex |
| author_facet | Milad Zeraatpisheh Stephane P.A. Bordas Lars A.A. Beex |
| author_sort | Milad Zeraatpisheh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T04:51:19Z |
| format | Article |
| id | doaj.art-3d7bb6dc90e341549867ffd2c2179755 |
| institution | Directory Open Access Journal |
| issn | 2632-6736 |
| language | English |
| last_indexed | 2024-04-10T04:51:19Z |
| publishDate | 2021-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj.art-3d7bb6dc90e341549867ffd2c21797552023-03-09T12:31:49ZengCambridge University PressData-Centric Engineering2632-67362021-01-01210.1017/dce.2021.9Bayesian model uncertainty quantification for hyperelastic soft tissue modelsMilad Zeraatpisheh0https://orcid.org/0000-0002-0019-1383Stephane P.A. Bordas1https://orcid.org/0000-0001-7622-2193Lars A.A. Beex2https://orcid.org/0000-0002-0486-6624Institute of Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Maison du Nombre, 6, Avenue de la Fonte, 4364 Esch-sur-Alzette, LuxembourgInstitute of Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Maison du Nombre, 6, Avenue de la Fonte, 4364 Esch-sur-Alzette, Luxembourg School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Cardiff CF243AA, United KingdomInstitute of Computational Engineering, Faculty of Science, Technology and Communication, University of Luxembourg, Maison du Nombre, 6, Avenue de la Fonte, 4364 Esch-sur-Alzette, LuxembourgPatient-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.https://www.cambridge.org/core/product/identifier/S2632673621000095/type/journal_articleModel uncertaintyBayesian inferenceincompressible hyperelasticitysoft tissues |
| spellingShingle | Milad Zeraatpisheh Stephane P.A. Bordas Lars A.A. Beex Bayesian model uncertainty quantification for hyperelastic soft tissue models Data-Centric Engineering Model uncertainty Bayesian inference incompressible hyperelasticity soft tissues |
| title | Bayesian model uncertainty quantification for hyperelastic soft tissue models |
| title_full | Bayesian model uncertainty quantification for hyperelastic soft tissue models |
| title_fullStr | Bayesian model uncertainty quantification for hyperelastic soft tissue models |
| title_full_unstemmed | Bayesian model uncertainty quantification for hyperelastic soft tissue models |
| title_short | Bayesian model uncertainty quantification for hyperelastic soft tissue models |
| title_sort | bayesian model uncertainty quantification for hyperelastic soft tissue models |
| topic | Model uncertainty Bayesian inference incompressible hyperelasticity soft tissues |
| url | https://www.cambridge.org/core/product/identifier/S2632673621000095/type/journal_article |
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