A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains
This work addresses the challenge of constitutive modeling for anisotropic inelastic materials at finite strains, which is incremental by building on existing neural network methods with physical constraints.
The authors tackled the problem of modeling anisotropic inelasticity at finite strains by augmenting neural networks with material principles, resulting in accurate and stable performance beyond the training regime in deformation and reaction force predictions.
We propose a complement to constitutive modeling that augments neural networks with material principles to capture anisotropy and inelasticity at finite strains. The key element is a dual potential that governs dissipation, consistently incorporates anisotropy, and-unlike conventional convex formulations-satisfies the dissipation inequality without requiring convexity. Our neural network architecture employs invariant-based input representations in terms of mixed elastic, inelastic and structural tensors. It adapts Input Convex Neural Networks, and introduces Input Monotonic Neural Networks to broaden the admissible potential class. To bypass exponential-map time integration in the finite strain regime and stabilize the training of inelastic materials, we employ recurrent Liquid Neural Networks. The approach is evaluated at both material point and structural scales. We benchmark against recurrent models without physical constraints and validate predictions of deformation and reaction forces for unseen boundary value problems. In all cases, the method delivers accurate and stable performance beyond the training regime. The neural network and finite element implementations are available as open-source and are accessible to the public via https://doi.org/10.5281/zenodo.17199965.