New framework learns material models using neural networks and auto-differentiation

By PulseAugur Editorial · [1 sources] · 2026-06-06 04:00

Researchers have developed FE-MAD, a novel framework that integrates constitutive neural networks with a JAX-FEM nonlinear solver. This end-to-end differentiable system uses automatic differentiation to compute gradients, enabling efficient learning of material models from full-field deformation data. The framework was successfully demonstrated on hyperelasticity problems, identifying material properties from various experimental datasets and generalizing to unseen samples. AI

IMPACT This framework could accelerate the discovery and design of new materials by enabling more efficient learning of complex constitutive models from experimental data.

RANK_REASON This is a research paper detailing a new computational framework for material learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

paper
other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

arXiv cs.AI TIER_1 English(EN) · Matthias Knipper, Chenyi Ji, Malte Brand, Kevin Linka · 2026-06-06 04:00

Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data

arXiv:2606.05199v1 Announce Type: cross Abstract: The identification of constitutive neural network models from heterogeneous full-field deformation data provides a robust alternative to traditional calibration methods based on homogeneous stress-strain experiments, particularly …

COVERAGE [1]

Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data

RELATED TOPICS