30 citations · 52 across the 22 of their papers we have counts for
12 papers · 1 filter
Learning Discrete Riemannian Metrics for Physical Fields with Cochain-Frame Equivariance
Dongzhe Zheng, Christine Allen-Blanchette
Physical fields on meshes require a separation between topology and geometry: differential identities should remain exact, while geometry, material response, and anisotropic coupli…
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…
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…
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…
Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation
Tao Zhong, Yixun Hu, Dongzhe Zheng +2
Inverse problems for stiff parabolic partial differential equations (PDEs), such as the inverse heat conduction problem (IHCP), are severely ill-posed: the forward map rapidly damp…
Discovering Phase Space Structure in Learned Hamiltonian Systems
Jiayin Liu, Yulong Yang, Vineet Bansal +1
Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration space, fixed physical parameters, and de…
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…
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…
Behavior-Inspired Neural Networks for Relational Inference
Yulong Yang, Bowen Feng, Keqin Wang +3
From pedestrians to Kuramoto oscillators, interactions between agents govern how dynamical systems evolve in space and time. Discovering how these agents relate to each other has t…
Hamiltonian GAN
Christine Allen-Blanchette
A growing body of work leverages the Hamiltonian formalism as an inductive bias for physically plausible neural network based video generation. The structure of the Hamiltonian ens…
LagNetViP: A Lagrangian Neural Network for Video Prediction
Christine Allen-Blanchette, Sushant Veer, Anirudha Majumdar +1
The dominant paradigms for video prediction rely on opaque transition models where neither the equations of motion nor the underlying physical quantities of the system are easily i…