用图神经网络模拟太阳风驱动的地磁环境，实现快速预报与不确定性量化。
Graph-based Neural Space Weather Forecasting
- 基于图神经网络构建神经模拟器，从Vlasiator数据中学习空间天气演化规律。
- 预测速度比原模型快数千倍，且可生成多组预报以量化不确定性。
- 适合需要实时预报和风险评估的航天、通信等领域的研究人员使用。
精准的空间天气预报对保护日益数字化的基础设施至关重要。混合-Vlasov模型（如Vlasiator）在物理真实性上超越现有业务系统，但计算成本过高，难以实时应用。本文提出一种基于图神经网络的神经模拟器，利用Vlasiator数据进行训练，可自回归预测由上游太阳风驱动的近地空间条件。我们展示了如何实现快速确定性预报，并通过生成模型生成集合预报以捕捉预测不确定性。该工作表明，机器学习为现有空间天气预测系统引入了不确定性量化能力，并使混合-Vlasov模拟具备实际业务应用的可行性。
原文摘要 · Abstract (English)
Accurate space weather forecasting is crucial for protecting our increasingly digital infrastructure. Hybrid-Vlasov models, like Vlasiator, offer physical realism beyond that of current operational systems, but are too computationally expensive for real-time use. We introduce a graph-based neural emulator trained on Vlasiator data to autoregressively predict near-Earth space conditions driven by an upstream solar wind. We show how to achieve both fast deterministic forecasts and, by using a generative model, produce ensembles to capture forecast uncertainty. This work demonstrates that machine learning offers a way to add uncertainty quantification capability to existing space weather prediction systems, and make hybrid-Vlasov simulation tractable for operational use.
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