用神经网络快速预测太阳风速度，比传统方法更快更准。
Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator
- 基于球面傅里叶神经算子构建自回归模型，逐段外推太阳风速度。
- 在远距离区域预测精度优于单步模型，性能媲美或超越传统数值模型。
- 适合需要快速迭代边界条件的太阳风暴预报研究者使用。
太阳风是来自太阳日冕的持续带电粒子流，塑造了日球层并影响近地空间系统。准确预测高速流和日冕物质抛射等特征对空间天气预报至关重要，但传统的三维磁流体（MHD）模型计算成本高，难以快速探索边界条件不确定性。本文首次提出基于球面傅里叶神经算子（SFNO）的自回归机器学习代理模型，用于稳态太阳风径向速度建模。通过预测有限径向范围并逐次向外传播解，该模型在远距离区域的预测精度优于单步方法。与数值HUX代理模型相比，SFNO表现相当或更优，同时提供灵活、可训练、数据驱动的新范式，为高保真太阳风建模开辟新路径。源代码与可视化结果详见https://github.com/rezmansouri/solarwind-sfno-velocity-autoregressive。
原文摘要 · Abstract (English)
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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