Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator

• URL: http://arxiv.org/abs/2511.20830v1
• Date: Tue, 25 Nov 2025 20:30:03 GMT
• Title: Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator
• Authors: Reza Mansouri, Dustin Kempton, Pete Riley, Rafal Angryk,
• Abstract summary: We introduce the first autoregressive machine learning surrogate for steady-state solar wind radial velocity.<n>The model improves accuracy in distant regions compared to a single-step approach.<n>Compared with the numerical HUX surrogate, SFNO demonstrates superior or comparable performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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