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
Advances in deep neural networks and increased computational capabilities are enabling more sophisticated approaches to long-range forecasting in complex systems.
Accurate long-range forecasting for geophysical systems is crucial for climate modeling, disaster preparedness, resource management, and strategic planning.
This development proposes a methodology to overcome the common drift and instability issues of AI models in long-horizon predictions, potentially making AI emulators more reliable for extended forecasts.
- · Climate scientists
- · Geophysical research institutions
- · AI model developers
- · Sustainable resource management
- · Traditional high-cost full-physics simulations
- · Organizations relying on short-term reactive planning
Improved accuracy and stability of AI-driven long-term environmental and geophysical predictions.
Enhanced capabilities for proactive policy-making and infrastructure development based on reliable multi-decade forecasts.
Reduced economic and social costs associated with climate change and natural disasters due to better foresight and mitigation strategies.
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Read at arXiv cs.LG