9 papers
Frequency-Separable Hamiltonian Neural Network for Multi-Timescale Dynamics
Yaojun Li, Yulong Yang, Christine Allen-Blanchette
While Hamiltonian mechanics provides a powerful inductive bias for neural networks modeling dynamical systems, Hamiltonian Neural Networks and their variants often fail to capture…
Physically Plausible Multi-System Trajectory Generation and Symmetry Discovery
Jiayin Liu, Yulong Yang, Vineet Bansal +1
From metronomes to celestial bodies, mechanics underpins how the world evolves in time and space. With consideration of this, a number of recent neural network models leverage indu…
Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schrödinger Bridges
Tao Zhong, Jonah Buchanan, Christine Allen-Blanchette
We propose a new approach to vision-based dexterous grasp translation, which aims to transfer grasp intent across robotic hands with differing morphologies. Given a visual observat…
Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders
Fynn Fromme, Hans Harder, Christine Allen-Blanchette +1
The use of machine learning for modeling, understanding, and controlling large-scale physics systems is quickly gaining in popularity, with examples ranging from electromagnetism o…
GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping
Tao Zhong, Christine Allen-Blanchette
We propose GAGrasp, a novel framework for dexterous grasp generation that leverages geometric algebra representations to enforce equivariance to SE(3) transformations. By encoding…
Resolving Oversmoothing with Opinion Dissensus
Keqin Wang, Yulong Yang, Ishan Saha +1
While graph neural networks (GNNs) have allowed researchers to successfully apply neural networks to non-Euclidean domains, deep GNNs often exhibit lower predictive performance tha…