A multi-horizon graph neural network emulator jointly predicts state increments for ice thickness and velocities at several lead times and shows higher long-range accuracy and stability than autoregressive or direct baselines on Pine Island Glacier simulations.
Proceedings of the National Academy of Sciences116(4), 1095– 1103 (2019)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting
A multi-horizon graph neural network emulator jointly predicts state increments for ice thickness and velocities at several lead times and shows higher long-range accuracy and stability than autoregressive or direct baselines on Pine Island Glacier simulations.