Training an AI hyperelastic constitutive model with experimental data

Clément Jailin; Antoine Benady; Emmanuel Baranger

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arXiv · 2410.16304

Training an AI hyperelastic constitutive model with experimental data

Clément Jailin·Antoine Benady·Emmanuel Baranger

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Abstract

A Physics-Augmented Neural network is trained to model a hyperelastic behavior. The dataset used for the training, validation, and test are displacement-force couples obtained from two experiments on a rubber-like material. One experiment was dedicated for the test, to assess the capacity of the model to generalize on unseen loadings and geometries. The trained AI model outperforms a standard Neo Hookean model identified on the same data. Particular attention is paid to the mechanical data information contained in the different datasets.

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Clément Jailin, Antoine Benady, Emmanuel Baranger. 2024-10-07. Training an AI hyperelastic constitutive model with experimental data. https://arxiv.org/abs/2410.16304

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