Use of a genetic algorithm for determining material parameters in ventricular myocardium

Nair, Arun U.; Taggart, David G.; Vetter, Frederick J.

doi:10.1109/nebc.2004.1300057

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…They are capable of establishing the relationship among variables based on the existing data, which is different from the traditional mechanical analysis method. Artificial intelligence has shown its advantages in the prediction of mechanical parameters, optimization of mechanical models, health monitoring and many other aspects [ 26 , 27 , 28 , 29 , 30 ], and has become the focus of many researchers and the trend of development. For instance, Nair et al [ 30 ] inversed the constitutive parameters of soft biological materials based on genetic algorithm, numerical simulation and experimental testing deformation.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence has shown its advantages in the prediction of mechanical parameters, optimization of mechanical models, health monitoring and many other aspects [ 26 , 27 , 28 , 29 , 30 ], and has become the focus of many researchers and the trend of development. For instance, Nair et al [ 30 ] inversed the constitutive parameters of soft biological materials based on genetic algorithm, numerical simulation and experimental testing deformation. By using the back propagation neural network optimized with genetic algorithm, Li et al [ 31 ] predicted the values of dynamic stiffness and loss factor varied with the different frequency and discussed the frequency dependence of rubber bushing.…”

Section: Introductionmentioning

confidence: 99%

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Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network

Hou

Zhang

et al. 2022

Materials

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In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.

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