Experimental Learning of a Hyperelastic Behavior with a Physics-Augmented Neural Network

Background:

The recent development of Physics-Augmented Neural Networks (PANN) opens new opportunities for modeling material behaviors. These approaches have demonstrated their efficiency when trained on synthetic cases.

Objective:

This study aims to demonstrate the effectiveness of training PANN using real experimental data for modeling hyperelastic behavior.

Methods:

The approach involved two uni-axial experiments equipped with digital image correlation and force sensors. The tests achieved axial deformations exceeding 200% and presented non-linear responses. Twenty loading steps extracted from one experiment were used to train the PANN. The model architecture was optimized based on results from a validation dataset, utilizing equilibrium gap loss computed on six loading steps. Finally, 544 loading steps from the first experiment and 80 steps from a second independent experiment were used for testing purposes.

Results:

The PANN model effectively captured the hyperelastic behavior across and beyond the training loads, showing superior performance compared to the standard Neo-Hookean model when assessed using various evaluation metrics.

Conclusions:

Training PANN with experimental mechanical data shows promising results, outperforming traditional modeling approaches.