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Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture.
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations
We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations.
Model reduction for the material point method via an implicit neural representation of the deformation map
Our technique approximates the $\textit{kinematics}$ by approximating the deformation map using an implicit neural representation that restricts deformation trajectories to reside on a low-dimensional manifold.
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