3 papers
cs.CE2026
A Differentiable Framework for Gradient Enhanced Damage with Physics-Augmented Neural Networks in JAX-FEM
Mark Wilkinson, Amirhossein Amiri-Hezaveh, Adrian Buganza Tepole
Soft materials such as rubbers, hydrogels, and biological tissues undergo damage in the form of stiffness degradation without apparent changes in their stress-free geometry. Accura…
cs.LG2025
Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
Vahidullah Taç, Amirhossein Amiri-Hezaveh, Manuel K. Rausch +3
We propose a new framework for identifying mechanical properties of heterogeneous materials without a closed-form constitutive equation. Given a full-field measurement of the displ…
physics.app-ph2025
A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids
Amirhossein Amiri-Hezaveh, Adrian Buganza Tepole
We propose a data-driven constitutive framework for anisotropic damage mechanics based on the second-order damage tensor approach for both compressible and incompressible materials…