Can I read Physics-augmented Neural Networks for Constitutive Modeling of Hyperelastic Geometrically Exact Beams on EtoBox?

Physics-augmented Neural Networks for Constitutive Modeling of Hyperelastic Geometrically Exact Beams by Schommartz, Jasper O.; Klein, Dominik K.; Cobo, Juan C. Alzate; Weeger, Oliver is a scholarly article available to read on EtoBox.

What is Physics-augmented Neural Networks for Constitutive Modeling of Hyperelastic Geometrically Exact Beams about?

We present neural network-based constitutive models for hyperelastic geometrically exact beams. The proposed models are physics-augmented, i.e., formulated to fulfill important mechanical conditions by construction, which improves accuracy and generalization. Strains and curvatures of the beam are used as input for feed-forward neural networks that represent the effective hyperelastic beam potential. Forces and moments are received as the gradients of the beam potential, ensuring thermodynamic consistency. Normalization conditions are considered via additional projection terms. Symmetry conditions are implemented by an invariant-based approach for transverse isotropy and a more flexible point symmetry constraint, which is included in transverse isotropy but poses fewer restrictions on the constitutive response. Furthermore, a data augmentation approach is proposed to improve the scaling behavior of the models for varying cross-section radii. Additionally, we introduce a parameterization with a scalar parameter to represent ring-shaped cross-sections with different ratios between the inner and outer radii. Formulating the beam potential as a neural network provides a highly flexible

Author

Schommartz, Jasper O.; Klein, Dominik K.; Cobo, Juan C. Alzate; Weeger, Oliver

Published

2024

Language

EN