11 citations · 20 across the 9 of their papers we have counts for
11 papers
Multi-Label Proportion Learning for Sea-Ice Type Prediction
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Sea-ice type prediction is important for climate monitoring, maritime navigation, and decision-making in polar regions. The main source of label data for this task is the ice chart…
Uncertainty-Aware Learning from Multi-Expert Interval Targets
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same observation. Such variation acr…
Uncertainty-Aware Sea-Ice Type Mapping with Multiple Ice Charts
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Sea-ice stage of development (SoD) describes the age and associated thickness of sea ice and provides important information for navigation, and operational ice monitoring. SoD labe…
An Autonomous GeoAI Agent for Arctic Eco-Navigation
Samira Alkaee Taleghan, Younghyun Koo, Farnoush Banaei-Kashani
Arctic maritime navigation is becoming increasingly important as changing sea-ice conditions expand seasonal accessibility while simultaneously introducing substantial operational,…
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 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…
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…
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…