Work

A note on constitutive artificial neural networks for finite strain plasticity

Year: 2026

Type: article

Abstract: 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.

Source: Mechanics Research Communications

DOI: 10.1016/j.mechrescom.2026.104707

Authors: Tim van der Velden, Birte Boes, Tim Brepols, Ellen Kuhl, Hagen Holthusen

Institutions: RWTH Aachen University, University of Wuppertal, Friedrich-Alexander-Universität Erlangen-Nürnberg, Stanford University

Language: English

Topic: Elasticity and Material Modeling

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,580

Funders: National Science Foundation, Deutsche Forschungsgemeinschaft, European Research Council, H2020 European Research Council