{"ID":2837179,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20830","arxiv_id":"2511.20830","title":"Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator","abstract":"The solar wind, a continuous outflow of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is critical for space weather forecasting, but traditional three-dimensional magnetohydrodynamic (MHD) models are computationally expensive, limiting rapid exploration of boundary condition uncertainties. We introduce the first autoregressive machine learning surrogate for steady-state solar wind radial velocity using the Spherical Fourier Neural Operator (SFNO). By predicting a limited radial range and iteratively propagating the solution outward, the model improves accuracy in distant regions compared to a single-step approach. Compared with the numerical HUX surrogate, SFNO demonstrates superior or comparable performance while providing a flexible, trainable, and data-driven alternative, establishing a novel methodology for high-fidelity solar wind modeling. The source code and additional visual results are available at https://github.com/rezmansouri/solarwind-sfno-velocity-autoregressive.","short_abstract":"The solar wind, a continuous outflow of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is critical for space weather forecasting, but traditional three-dimensional magnetohydrodyn...","url_abs":"https://arxiv.org/abs/2511.20830","url_pdf":"https://arxiv.org/pdf/2511.20830v1","authors":"[\"Reza Mansouri\",\"Dustin Kempton\",\"Pete Riley\",\"Rafal Angryk\"]","published":"2025-11-25T20:30:03Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"LoRA\"]","has_code":false,"code_links":[{"ID":606663,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2837179,"paper_url":"https://arxiv.org/abs/2511.20830","paper_title":"Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator","repo_url":"https://github.com/rezmansouri/solarwind-sfno-velocity-autoregressive","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}