7 papers
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…
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…
Hierarchical Information-sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity
Younghyun Koo, Maryam Rahnemoonfar
Forecasting sea ice concentration (SIC) and sea ice velocity (SIV) in the Arctic Ocean is of great significance as the Arctic environment has been changed by the recent warming cli…
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…