Molecular Dynamics Data-driven Hyperelastic Constitutive Modeling Using MLP, GR, and RBF Models of Artificial Neural Network

초록

In this study, three neural network models are used to establish neural network constitutive models (NNCMs) for hyperelastic materials: multilayer perceptron neural network, generalized regression neural network, and radial basis function neural network (RBFNN). We focused on artificial neural network training of the dataset: full atomistic molecular dynamics simulation results. We obtained the optimal hyperparameters empirically for each neural network model to construct an optimal NNCM. The three models above were compared based on statistical error analysis. The results indicated that the RBFNN model had the highest accuracy in terms of the root-mean-square error, mean absolute error, and regression values. We applied the proposed NNCM model to data-driven computational mechanics to verify the proposed NNCM model, with the data-driven solver demonstrating superior performance of RBFNN-based NNCM over those of the other neural network models.

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