Toward Data-Driven Surrogates of the Solar Wind with Spherical Fourier Neural Operator
This work addresses the need for efficient real-time space weather forecasting to mitigate disruptions to satellites, power grids, and communications, though it is incremental as it builds on existing surrogate modeling approaches.
The paper tackles the problem of computationally expensive 3D magnetohydrodynamic models for solar wind simulation by developing a surrogate model using a Spherical Fourier Neural Operator (SFNO), achieving comparable or better performance than an existing numerical surrogate (HUX) across several metrics.
The solar wind, a continuous stream of charged particles from the Sun's corona, shapes the heliosphere and impacts space systems near Earth. Variations such as high-speed streams and coronal mass ejections can disrupt satellites, power grids, and communications, making accurate modeling essential for space weather forecasting. While 3D magnetohydrodynamic (MHD) models are used to simulate and investigate these variations in the solar wind, they tend to be computationally expensive, limiting their usefulness in investigating the impacts of boundary condition uncertainty. In this work, we develop a surrogate for steady state solar wind modeling, using a Spherical Fourier Neural Operator (SFNO). We compare our model to a previously developed numerical surrogate for this task called HUX, and we show that the SFNO achieves comparable or better performance across several metrics. Though HUX retains advantages in physical smoothness, this underscores the need for improved evaluation criteria rather than a flaw in SFNO. As a flexible and trainable approach, SFNO enables efficient real-time forecasting and can improve with more data. The source code and more visual results are available at https://github.com/rezmansouri/solarwind-sfno-velocity.