From the 1 of 7 linked papers with an AI index.
7 papers
Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling
Yulong Yang, Clara O'Farrell, Christine Allen-Blanchette
The paper introduces SPar-GAN, a physics‑aware generative adversarial network that learns and predicts parachute pitch‑yaw dynamics by conditioning on canopy design and freestream…
A Hypertoroidal Covering for Perfect Color Equivariance
Yulong Yang, Zhikun Xu, Yaojun Li +1
When the color distribution of input images changes at inference, the performance of conventional neural network architectures drops considerably. A few researchers have begun to i…
Learning Color Equivariant Representations
Yulong Yang, Felix O'Mahony, Christine Allen-Blanchette
In this paper, we introduce group convolutional neural networks (GCNNs) equivariant to color variation. GCNNs have been designed for a variety of geometric transformations from 2D…
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…
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…