5 citations · 14 across the 17 of their papers we have counts for
17 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…
ST-GRIT: Spatio-Temporal Graph Transformer For Internal Ice Layer Thickness Prediction
Zesheng Liu, Maryam Rahnemoonfar
Understanding the thickness and variability of internal ice layers in radar imagery is crucial for monitoring snow accumulation, assessing ice dynamics, and reducing uncertainties…
GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction
Zesheng Liu, Maryam Rahnemoonfar
Gaining a deeper understanding of the thickness and variability of internal ice layers in Radar imagery is essential in monitoring the snow accumulation, better evaluating ice dyna…
Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction
Zesheng Liu, Maryam Rahnemoonfar
Understanding spatio-temporal patterns in polar ice layers is essential for tracking changes in ice sheet balance and assessing ice dynamics. While convolutional neural networks ar…
Graph Neural Network as Computationally Efficient Emulator of Ice-sheet and Sea-level System Model (ISSM)
Younghyun Koo, Maryam Rahnemoonfar
The Ice-sheet and Sea-level System Model (ISSM) provides solutions for Stokes equations relevant to ice sheet dynamics by employing finite element and fine mesh adaption. However,…
Graph Neural Networks for Emulation of Finite-Element Ice Dynamics in Greenland and Antarctic Ice Sheets
Younghyun Koo, Maryam Rahnemoonfar
Although numerical models provide accurate solutions for ice sheet dynamics based on physics laws, they accompany intensified computational demands to solve partial differential eq…
Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network
Zesheng Liu, Maryam Rahnemoonfar
Learning spatio-temporal patterns of polar ice layers is crucial for monitoring the change in ice sheet balance and evaluating ice dynamic processes. While a few researchers focus…
Physics-Informed Machine Learning On Polar Ice: A Survey
Zesheng Liu, YoungHyun Koo, Maryam Rahnemoonfar
The mass loss of the polar ice sheets contributes considerably to ongoing sea-level rise and changing ocean circulation, leading to coastal flooding and risking the homes and livel…
Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling
Maryam Rahnemoonfar, Younghyun Koo
The Ice-sheet and Sea-level System Model (ISSM) provides numerical solutions for ice sheet dynamics using finite element and fine mesh adaption. However, considering ISSM is compat…
Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks
Benjamin Zalatan, Maryam Rahnemoonfar
As we deal with the effects of climate change and the increase of global atmospheric temperatures, the accurate tracking and prediction of ice layers within polar ice sheets grows…
Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach
Benjamin Zalatan, Maryam Rahnemoonfar
The precise tracking and prediction of polar ice layers can unveil historic trends in snow accumulation. In recent years, airborne radar sensors, such as the Snow Radar, have been…
Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar
Benjamin Zalatan, Maryam Rahnemoonfar
The accurate prediction and estimation of annual snow accumulation has grown in importance as we deal with the effects of climate change and the increase of global atmospheric temp…