基于神经算子的太阳内边界状态多场重构
Neural Operator based Multi-Field Reconstruction of Inner Solar Boundary State
- Georgia State University（佐治亚州立大学）
- Predictive Sciences Inc.（预测科学公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究采用局部神经算子（LocalNO），基于太阳30个太阳半径处的径向速度与径向磁场，重构太阳磁流体动力学的多场多尺度内边界状态，以支撑日球层建模与太阳风预测。
AI中文摘要：
太阳风是从太阳表面喷出的带电粒子连续流，受复杂且相互作用的磁流体动力学过程支配。内边界条件的精确设定对日球层建模和太阳风预测至关重要。在许多实际应用中，仅能直接获取相互作用多场变量的子集，但为了全面开展太阳风预测及下游磁流体动力学模拟，需要更完整的边界状态。本研究利用算子学习解决30个太阳半径（$R_\u2609$）处太阳磁流体动力学状态的多场多尺度学习问题：给定径向速度和径向磁场，目标是重构非径向速度与磁场分量、径向及非径向电流密度、热力学密度和压强分量。该映射高度非线性、空间耦合且多尺度，对数据驱动的科学机器学习构成挑战。为解决此问题，采用Local Neural Operator（LocalNO，局部神经算子），其可学习输入与输出函数空间间的映射，同时保留局部性与分辨率感知能力。与传统回归模型和自编码器模型不同，神经算子更适合学习物理系统产生的结构化场到场变换。所得预测结果连同输入，将用作未来内日球层建模流程的边界条件变量。
英文摘要：
The Solar wind is a continuous flow of charged particles emanating from the solar surface and governed by complex, interacting magnetohydrodynamic processes. Accurate specification of inner-boundary conditions is essential for heliospheric modeling and solar-wind prediction. In many practical applications, only a subset of interacting multi-field variables is directly available, but for a comprehensive view of solar wind prediction and downstream magnetohydrodynamic simulations, a more complete boundary state is required. In this work, we study the problem of learning the multi-field multi-scale solar magnetohydrodynamic state at 30 solar radii ($R_\odot$) using operator learning. Specifically, given the radial velocity and radial magnetic field, we aim to reconstruct the non-radial velocity and magnetic field components, radial and non-radial current density, thermodynamic density, and pressure components. This mapping is highly nonlinear, spatially coupled, and multi-scale, making it a challenging task for data-driven scientific machine learning. To address this problem, we employ a Local Neural Operator (LocalNO) that learns mappings between input and output function spaces while retaining locality and resolution-awareness. Unlike conventional regression models and autoencoder models, neural operators are better suited for learning structured field-to-field transformations arising from physical systems. The resulting predictions along with inputs are intended to serve as boundary condition variables for future inner-heliospheric modeling pipelines.
