8 citations · 20 across the 6 of their papers we have counts for
6 papers
An Operator-Consistent Graph Neural Network for Learning Diffusion Dynamics on Irregular Meshes
Yuelian Li, Andrew Rushing Hands
Classical numerical methods solve partial differential equations (PDEs) efficiently on regular meshes, but many of them become unstable on irregular domains. In practice, multiphys…
Self-supervised Learning of Latent Space Dynamics
Yue Li, Gene Wei-Chin Lin, Egor Larionov +7
Modeling the dynamic behavior of deformable objects is crucial for creating realistic digital worlds. While conventional simulations produce high-quality motions, their computation…
LBM-GNN: Graph Neural Network Enhanced Lattice Boltzmann Method
Yue Li
In this paper, we present LBM-GNN, a novel approach that enhances the traditional Lattice Boltzmann Method (LBM) with Graph Neural Networks (GNNs). We apply this method to fluid dy…
Differentiable Voronoi Diagrams for Simulation of Cell-Based Mechanical Systems
Logan Numerow, Yue Li, Stelian Coros +1
Navigating topological transitions in cellular mechanical systems is a significant challenge for existing simulation methods. While abstract models lack predictive capabilities at…
Differentiable Geodesic Distance for Intrinsic Minimization on Triangle Meshes
Yue Li, Logan Numerow, Bernhard Thomaszewski +1
Computing intrinsic distances on discrete surfaces is at the heart of many minimization problems in geometry processing and beyond. Solving these problems is extremely challenging…
Neural Metamaterial Networks for Nonlinear Material Design
Yue Li, Stelian Coros, Bernhard Thomaszewski
Nonlinear metamaterials with tailored mechanical properties have applications in engineering, medicine, robotics, and beyond. While modeling their macromechanical behavior is chall…