Toward Data-Driven Surrogates of the Solar Wind with Spherical Fourier Neural Operator

TL;DR

This paper introduces a Spherical Fourier Neural Operator as a data-driven surrogate model for steady state solar wind simulation, offering comparable or better performance than existing methods and enabling efficient real-time space weather forecasting.

Contribution

The paper presents the first application of SFNO to solar wind modeling, demonstrating its effectiveness and potential for real-time space weather prediction.

Findings

SFNO achieves comparable or better performance than HUX in several metrics.

SFNO enables efficient real-time forecasting of solar wind conditions.

The source code is publicly available at the provided GitHub link.

Abstract

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.