Search Results for author:
Found 6 papers, 3 papers with code
Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces
We propose a self-supervised approach for learning physics-based subspaces for real-time simulation.
Neural Metamaterial Networks for Nonlinear Material Design
Nonlinear metamaterials with tailored mechanical properties have applications in engineering, medicine, robotics, and beyond.
HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics
We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics.
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations
Recent advances in implicit neural representations show great promise when it comes to generating numerical solutions to partial differential equations.
Deep Physics-aware Inference of Cloth Deformation for Monocular Human Performance Capture
Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera.
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact
We present a differentiable dynamics solver that is able to handle frictional contact for rigid and deformable objects within a unified framework.
