Graph-based Neural Space Weather Forecasting
This work addresses the need for real-time, uncertainty-aware space weather predictions to protect digital infrastructure, representing an incremental improvement by making hybrid-Vlasov models tractable for operational use.
The paper tackles the problem of computationally expensive space weather forecasting by introducing a graph-based neural emulator trained on Vlasiator data to predict near-Earth space conditions, achieving fast deterministic forecasts and ensemble-based uncertainty quantification.
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.