From the 1 of 16 linked papers with an AI index.
16 papers
Learning Discrete Riemannian Metrics for Physical Fields with Cochain-Frame Equivarianc
Dongzhe Zheng, Christine Allen-Blanchette
Physical fields on meshes require a separation between topology and geometry: conservation laws are topological and should be exact, while geometry, material response, and anisotro…
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…
Topology-Preserving Neural Operator Learning via Hodge Decomposition
Dongzhe Zheng, Tao Zhong, Christine Allen-Blanchette
In this paper, we study solution operators of physical field equations on geometric meshes from a function-space perspective. We reveal that Hodge orthogonality fundamentally resol…
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…
HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts
Tao Zhong, Dongzhe Zheng, Christine Allen-Blanchette
Sparse Mixture-of-Experts (MoE) layers route tokens through a handful of experts, and learning-free compression of these layers reduces inference cost without retraining. A subtle…
Neural Fields for NV-Center Inverse Sensing
Zhixuan Zhao, Tao Zhong, Yixun Hu +2
Inverse problems in scientific sensing are often solved with either hand-designed regularizers or supervised networks trained on simulated labels, yet both can fail when the forwar…