Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator
This provides a more efficient method for space weather forecasting, which is critical for protecting space systems near Earth, though it appears incremental as it builds on existing surrogate modeling approaches.
The authors tackled the problem of computationally expensive solar wind modeling by developing an autoregressive machine learning surrogate using the Spherical Fourier Neural Operator (SFNO), which demonstrated superior or comparable performance to existing numerical surrogates while offering a flexible, data-driven alternative.
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.