Physics-augmented neural networks for constitutive modeling of hyperelastic geometrically exact beams

TL;DR

This paper introduces physics-augmented neural network models for hyperelastic beams that ensure mechanical consistency, improve accuracy, and generalize well across different cross-sectional geometries, enabling efficient surrogate modeling.

Contribution

The paper develops neural network-based constitutive models that incorporate physical constraints, symmetry, and data augmentation for hyperelastic beams with complex cross-sections, enhancing modeling efficiency and accuracy.

Findings

Models achieve high accuracy in predicting beam behavior.

Excellent generalization across various cross-sectional geometries.

Successful application in beam simulations demonstrates practical utility.

Abstract

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 model. This enables efficient constitutive surrogate modeling for geometrically exact beams with nonlinear material behavior and cross-sectional deformation, which otherwise would require computationally much more expensive methods. The models are calibrated and tested with data generated for beams with circular and ring-shaped hyperelastic deformable cross-sections at varying inner and outer radii, showing excellent accuracy and generalization. The applicability of the proposed point symmetric model is further demonstrated by applying it in beam simulations. In all studied cases, the proposed model shows excellent performance.