Graph-based Neural Space Weather Forecasting
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914097770004480 |
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| author | Holmberg, Daniel Zaitsev, Ivan Alho, Markku Bouri, Ioanna Franssila, Fanni Jeong, Haewon Palmroth, Minna Roos, Teemu |
| author_facet | Holmberg, Daniel Zaitsev, Ivan Alho, Markku Bouri, Ioanna Franssila, Fanni Jeong, Haewon Palmroth, Minna Roos, Teemu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19605 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-based Neural Space Weather Forecasting Holmberg, Daniel Zaitsev, Ivan Alho, Markku Bouri, Ioanna Franssila, Fanni Jeong, Haewon Palmroth, Minna Roos, Teemu Space Physics Machine Learning Plasma Physics 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. |
| title | Graph-based Neural Space Weather Forecasting |
| topic | Space Physics Machine Learning Plasma Physics |
| url | https://arxiv.org/abs/2509.19605 |