{"ID":2867421,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19605","arxiv_id":"2509.19605","title":"Graph-based Neural Space Weather Forecasting","abstract":"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.","short_abstract":"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 Vl...","url_abs":"https://arxiv.org/abs/2509.19605","url_pdf":"https://arxiv.org/pdf/2509.19605v2","authors":"[\"Daniel Holmberg\",\"Ivan Zaitsev\",\"Markku Alho\",\"Ioanna Bouri\",\"Fanni Franssila\",\"Haewon Jeong\",\"Minna Palmroth\",\"Teemu Roos\"]","published":"2025-09-23T21:53:35Z","proceeding":"physics.space-ph","tasks":"[\"physics.space-ph\",\"cs.LG\",\"physics.plasm-ph\"]","methods":"[]","has_code":false}