Physics-informed Neural Network for Force-free Magnetic Field Extrapolation  

作　　者：Yao Zhang Long Xu Yihua Yan 

机构地区：[1]State Key Laboratory of Space Weather,National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China [2]University of Chinese Academy of Sciences,Beijing 101408,China [3]Faculty of Information Science and Engineering,Ningbo University,Ningbo 315211,China

出　　处：《Research in Astronomy and Astrophysics》2024年第10期159-169,共11页天文和天体物理学研究（英文版）

基　　金：supported by the National Key R&D Program of China(Nos.2021YFA1600504,2022YFE0133700,2022YFF0503900);the National Natural Science Foundation of China(NSFC,Grant Nos.11790305 and 11973058)。

摘　　要：In this paper,we propose a physics-informed neural network extrapolation method that leverages machine learning techniques to reconstruct coronal magnetic fields.We enhance the classical neural network structure by introducing the concept of a quasi-output layer to address the challenge of preserving physical constraints during the neural network extrapolation process.Furthermore,we employ second-order optimization methods for training the neural network,which are more efficient compared to the first-order optimization methods commonly used in classical machine learning.Our approach is evaluated on the widely recognized semi-analytical model proposed by Low and Lou.The results demonstrate that the deep learning method achieves high accuracy in reconstructing the semianalytical model across multiple evaluation metrics.In addition,we validate the effectiveness of our method on the observed magnetogram of active region.

关 键 词：Sun:magnetic fields Sun:corona magnetohydrodynamics(MHD) 

分 类 号：P182[天文地球—天文学]