超弹性软组织模型贝叶斯模型不确定性量化

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-07-13 DOI:10.1017/dce.2021.9
Milad Zeraatpisheh, S. Bordas, L. Beex
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引用次数: 11

摘要

摘要特定于患者的外科模拟需要特定于患者识别组成参数。实验数据的稀疏性和数据中的大量噪声(例如,在手术期间恢复的)在识别中造成了相当大的不确定性。在这项探索性工作中,通过基于贝叶斯推理的概率识别方法来解决通常用于软组织的不可压缩超弹性的参数不确定性。我们的研究特别关注模型的不确定性:我们研究了当考虑不同形式的模型不确定性时,本构参数的已识别不确定性如何表现。模型的不确定性公式从无信息的公式到包含不可压缩超弹性更详细扩展的更精确的公式。研究表明,加入模型的不确定性可能会改善结果,但这并不能保证。
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Bayesian model uncertainty quantification for hyperelastic soft tissue models
Abstract 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.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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