论文标题
通过移动的操作员推断，预测从内螺球到地球的太阳风流
Predicting Solar Wind Streams from the Inner-Heliosphere to Earth via Shifted Operator Inference
论文作者
论文摘要
太阳风条件主要通过三维数值磁流失动力（MHD）模型预测。尽管他们能够产生高度准确的预测，但MHD模型仍需要计算密集的高维模拟。这使得它们不足以进行时间敏感的预测以及不确定性定量所需的大型分析。本文介绍了一种新的数据驱动的减少阶模型（ROM），以预测地球层太阳能风速。基于Galerkin投影的传统模型减少方法在较高的系统（例如太阳风）上遇到困难，因为它们需要大量的基础功能，并且可能变得不稳定。这项工作的核心贡献通过扩展非侵入操作员推理ROM框架来利用太阳旋转引起的太阳风中存在的翻译对称性来解决这一挑战。数值结果表明，我们的方法可以充分效仿MHD模拟，并优于降低的物理替代模型，即Heliospher upwind Oustwind Exuropolation模型。
Solar wind conditions are predominantly predicted via three-dimensional numerical magnetohydrodynamic (MHD) models. Despite their ability to produce highly accurate predictions, MHD models require computationally intensive high-dimensional simulations. This renders them inadequate for making time-sensitive predictions and for large-ensemble analysis required in uncertainty quantification. This paper presents a new data-driven reduced-order model (ROM) capability for forecasting heliospheric solar wind speeds. Traditional model reduction methods based on Galerkin projection have difficulties with advection-dominated systems -- such as solar winds -- since they require a large number of basis functions and can become unstable. A core contribution of this work addresses this challenge by extending the non-intrusive operator inference ROM framework to exploit the translational symmetries present in the solar wind caused by the Sun's rotation. The numerical results show that our method can adequately emulate the MHD simulations and outperforms a reduced-physics surrogate model, the Heliospheric Upwind Extrapolation model.
