When invariants matter: The role of I1 and I2 in neural network models of incompressible hyperelasticity

Abstract

For the formulation of machine learning-based material models, the usage of invariants of deformation tensors is attractive, since this can a priori guarantee objectivity and material symmetry. In this work, we consider incompressible, isotropic hyperelasticity, where two invariants I1 and I2 are required for depicting a deformation state. First, we aim at enhancing the understanding of the invariants. We provide an explicit representation of the set of invariants that are admissible, i.e. for which (I1, I2) a deformation state does indeed exist. Furthermore, we prove that uniaxial and equi-biaxial deformation correspond to the boundary of the set of admissible invariants. Second, we study how the experimentally-observed behaviour of different materials can be captured by means of neural network models of incompressible hyperelasticity, depending on whether both I1 and I2 or solely one of the invariants, i.e. either only I1 or only I2, are taken into account. To this end, we investigate three different experimental data sets from the literature. In particular, we demonstrate that considering only one invariant, either I1 or I2, can allow for good agreement with experiments in case of small deformations. In contrast, it is necessary to consider both invariants for precise models at large strains, for instance when rubbery polymers are deformed. Moreover, we show that multiaxial experiments are strictly required for the parameterisation of models considering I2. Otherwise, if only data from uniaxial deformation is available, significantly overly stiff responses could be predicted for general deformation states. On the contrary, I1-only models can make qualitatively correct predictions for multiaxial loadings even if parameterised only from uniaxial data, whereas I2-only models are completely incapable in even qualitatively capturing experimental stress data at large deformations.