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Found 7 papers, 0 papers with code
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.
How Can Large Language Models Help Humans in Design and Manufacturing?
The advancement of Large Language Models (LLMs), including GPT-4, provides exciting new opportunities for generative design.
Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics
Many NN approaches learn an end-to-end model that implicitly models both the governing PDE and constitutive models (or material models).
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology.
Implicit Neural Spatial Representations for Time-dependent PDEs
Whereas classical solvers can dynamically adapt their spatial representation only by resorting to complex remeshing algorithms, our INSR approach is intrinsically adaptive.
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.
