Automatic-Differentiation-Enabled Finite Element Model Updating: Learning Constitutive Neural Networks from Structural Deformation Data

arXiv:2606.05199 · physics.comp-ph, cs.AI · Submitted 2026-05-22 · Read on arXiv

physics.comp-ph, cs.AI

Submitted: 2026-05-22

Updated: 2026-10-09

Code: https://github.com/oogle/jax

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 1× in this paper · explained in 7 episodes
• Constitutive Artificial Neural Network — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Surrogate Model — 1× in this paper · explained in 2 episodes
• Surrogate Models — 1× in this paper · explained in 2 episodes

Sources

• Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
• Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data
• Input convex neural networks: universal approximation theorem and implementation for isotropic polyconvex hyperelastic energies
• Non-smooth optimization meets automated material model discovery

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