an:07189741
Zbl 1440.74229
Liu, Minliang; Liang, Liang; Sun, Wei
Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach
EN
Comput. Methods Appl. Mech. Eng. 347, 201-217 (2019).
00448459
2019
j
74L15 65D17 76Z05 74S99
machine learning; neural network; constitutive parameter estimation
Summary: The patient-specific biomechanical analysis of the aorta requires the quantification of the \textit{in vivo} mechanical properties of individual patients. Current inverse approaches have attempted to estimate the nonlinear, anisotropic material parameters from \textit{in vivo} image data using certain optimization schemes. However, since such inverse methods are dependent on iterative nonlinear optimization, these methods are highly computation-intensive. A potential paradigm-changing solution to the bottleneck associated with patient-specific computational modeling is to incorporate machine learning (ML) algorithms to expedite the procedure of \textit{in vivo} material parameter identification. In this paper, we developed an ML-based approach to estimate the material parameters from three-dimensional aorta geometries obtained at two different blood pressure (i.e., systolic and diastolic) levels. The nonlinear relationship between the two loaded shapes and the constitutive parameters is established by an ML-model, which was trained and tested using finite element (FE) simulation datasets. Cross-validations were used to adjust the ML-model structure on a training/validation dataset. The accuracy of the ML-model was examined using a testing dataset.