Physics-augmented neural networks for thermoelasticity

2024/09/23

In our latest publication, we extended the formulation of physics-augmented neural networks for constitutive modeling of thermo-hyperelastic materials.

Thanks to our co-authors Jan Niklas Fuhg from UT Austin and Reese Jones from Sandia national Labs, we extended the application of physics-augmented neural networks to thermo-hyperelastic material modeling.

Check out the publication in the Journal of the Mechanics and Physics of Solids (https://doi.org/10.1016/j.jmps.2024.105837) or on the pre-print on arXiv (https://arxiv.org/abs/2404.15562) for further details and applications!