快速且可微的太阳风磁流体力学代理模型：基于神经算子
Fast and Differentiable MHD Surrogate for Solar Wind using Neural Operator
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中文总结 AI 辅助
本文提出SNOW，一种基于神经算子的快速可微代理模型，用于全球MHD太阳风模拟，在单GPU上不到一秒生成完整三维解，并保持物理结构准确性。
中文摘要 AI 辅助
全球磁流体力学（MHD）模拟提供了日球层的完整等离子体状态，但其计算成本限制了需要重复模型评估的应用。我们引入了SNOW（太阳风神经算子），一个用于全球MHD太阳风的快速且可微的代理模型。SNOW结合了旋转弹道近似、球谐神经算子和因果径向上下文，通过连续的壳层间过渡，自回归地将所有八个MHD变量从20 Rs传播到240 Rs。使用覆盖一个太阳周期的AWSoM模拟进行训练和评估，SNOW在整个展开过程中保持稳定。该模型成功再现了大尺度的等离子体和磁场结构，包括快慢太阳风流、磁极性扇区、矢量方向、场线连通性和体流结构。此外，磁发散和感应诊断结果与参考MHD解相当。完整的二维解在单个GPU上不到一秒内生成。这些结果证明了快速神经仿真全球MHD太阳风解的可行性，同时保留了具有物理意义的等离子体和磁场结构。
英文摘要
Global magnetohydrodynamic (MHD) simulations provide the complete plasma state of the heliosphere, but their computational cost limits applications requiring repeated model evaluations. We introduce SNOW (Solar Neural Operator for Wind), a fast and differentiable surrogate for global MHD solar wind. SNOW combines a rotational ballistic approximation, a spherical harmonic neural operator, and causal radial context to autoregressively propagate all eight MHD variables from 20 to 240 Rs through consecutive shell to shell transitions. Trained and evaluated using AWSoM simulations spanning a solar cycle, SNOW remains stable across the complete rollout. The model successfully reproduces the large scale plasma and magnetic field structures, including fast and slow wind streams, magnetic polarity sectors, vector orientations, fieldline connectivity, and bulk-flow structures. Additionally, the magnetic divergence and induction diagnostics remain comparable to the reference MHD solutions. The complete three-dimensional solution is generated in less than one second on a single GPU. These results demonstrate the feasibility of fast neural emulation of global MHD solar wind solutions while retaining physically meaningful plasma and magnetic field structure.
发表机构
- University Corporation for Atmospheric Research（大气研究大学联盟）
- Space Weather TREC, University of Colorado（科罗拉多大学空间天气TREC）
- Queen Mary University of London（伦敦大学玛丽女王学院）
- National Center for Atmospheric Research（国家大气研究中心）
- University of Michigan（密歇根大学）
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