From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting

arXiv:2605.29952v1 Announce Type: new Abstract: 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 accumulation that arise when one-step autoregressive surrogates are rolled out over decades. Deep neural network can serve as efficient emulators, but most are trained only for next-step prediction and often drift or become unstable as the forecast horizon grows. We propose a multi-horizon graph neural network emulator that learns state-to-state transitions from a single curr

Source: arXiv cs.LG — read the full report at the original publisher.

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