Training an AI hyperelastic constitutive model with experimental data
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914983029243904 |
|---|---|
| author | Jailin, Clément Benady, Antoine Baranger, Emmanuel |
| author_facet | Jailin, Clément Benady, Antoine Baranger, Emmanuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training an AI hyperelastic constitutive model with experimental data Jailin, Clément Benady, Antoine Baranger, Emmanuel Computational Engineering, Finance, and Science 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. |
| title | Training an AI hyperelastic constitutive model with experimental data |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2410.16304 |