A note on constitutive artificial neural networks for finite strain plasticity

Data-driven approaches have led to structured constitutive artificial neural network formulations for inelastic phenomena such as viscoelasticity and growth, while the systematic extension to finite-strain plasticity remains an active area of research. In this communication, we extend the previously introduced inelastic constitutive artificial neural network framework to finite-strain elasto-visco-plasticity with combined nonlinear kinematic and isotropic hardening. The proposed formulation ensures thermodynamic consistency within a potential-based architecture by consistently integrating isotropic hardening and viscoplastic evolution mechanisms. In particular, a shared inelastic potential is employed for linear kinematic and isotropic hardening, while two additional potentials govern nonlinear kinematic hardening and the plastic yield function, enabling the modeling of non-associative plasticity. A JAX implementation of the extended framework and the associated simulation results are publicly available at Zenodo.org.