10 papers
COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
Zesheng Liu, Maryam Rahnemoonfar
In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes. COGENT e…
From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting
Zesheng Liu, Maryam Rahnemoonfar
Accurate long-range prediction of geophysical systems is difficult due to strongly nonlinear dynamics, the high computational cost of full-physics simulations, and the error accumu…
Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces
Zesheng Liu, Maryam Rahnemoonfar
Internal ice layers imaged by radar provide key evidence of snow accumulation and ice dynamics, but radar-derived layer boundary observations are often incomplete, with discontinuo…
GRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Prediction
Zesheng Liu, Maryam Rahnemoonfar
Graph transformers have demonstrated remarkable capability on complex spatio-temporal tasks, yet their depth is often limited by oversmoothing and weak long-range dependency modeli…
KAN-GCN: Combining Kolmogorov-Arnold Network with Graph Convolution Network for an Accurate Ice Sheet Emulator
Zesheng Liu, YoungHyun Koo, Maryam Rahnemoonfar
We introduce KAN-GCN, a fast and accurate emulator for ice sheet modeling that places a Kolmogorov-Arnold Network (KAN) as a feature-wise calibrator before graph convolution networ…
Prediction of Sea Ice Velocity and Concentration in the Arctic Ocean using Physics-informed Neural Network
Younghyun Koo, Maryam Rahnemoonfar
As an increasing amount of remote sensing data becomes available in the Arctic Ocean, data-driven machine learning (ML) techniques are becoming widely used to predict sea ice veloc…