Jaxfem
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Stewart Brand
Recommendedpaper · 2026-06-11
“This work presents FE-MAD (Finite Element-Based Material learning via Automatic Differentiation), an end-to-end differentiable framework that integrates a constitutive neural network model within a JAX-FEM nonlinear solver and identifies its parameters through gradient-based minimization of a measurement-mismatch loss.”
Differentiable Finite Element Framework for Learning Constitutive Models via Aut ↗