用球面傅里叶神经算子构建太阳风实时预测模型
Toward Data-Driven Surrogates of the Solar Wind with Spherical Fourier Neural Operator
- 基于球面傅里叶神经算子构建太阳风代理模型
- 性能媲美或优于传统数值模型HUX，支持实时预测
- 适合空间天气预报与边界条件不确定性研究
太阳风是来自日冕的带电粒子持续流，塑造了日球层并影响地球附近空间系统。高速流和日冕物质抛射等变化可能干扰卫星、电网和通信，因此精确建模对空间天气预报至关重要。尽管三维磁流体动力学（MHD）模型可用于模拟太阳风变化，但计算成本高，限制了其在边界条件不确定性影响研究中的应用。本文开发了一种稳态太阳风建模的代理模型，采用球面傅里叶神经算子（SFNO）。与此前的数值代理模型HUX对比，SFNO在多个指标上表现相当或更优。尽管HUX在物理平滑性上仍有优势，这凸显了需改进评估标准而非归因于SFNO缺陷。作为可训练且灵活的方法，SFNO支持高效实时预报，并能随数据增加而优化。源代码及更多可视化结果见https://github.com/rezmansouri/solarwind-sfno-velocity。
原文摘要 · Abstract (English)
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.
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