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

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外文摘要：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 semi-analytical model across multiple evaluation metrics.In addition,we validate the effectiveness of our method on the observed magnetogram of active region.

外文关键词：

Sunmagnetic fields-Suncorona-magnetohydrodynamics(MHD)

作者：

Yao Zhang、Long Xu、Yihua Yan

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作者单位：

State Key Laboratory of Space Weather,National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China

University of Chinese Academy of Sciences,Beijing 101408,China

Faculty of Information Science and Engineering,Ningbo University,Ningbo 315211,China

基金：

National Key R&D Program of ChinaNational Key R&D Program of ChinaNational Key R&D Program of ChinaNational Natural Science Foundation of China(NSFC)National Natural Science Foundation of China(NSFC)

项目编号：

2021YFA16005042022YFE01337002022 YFF05039001179030511973058

出版年：

2024

DOI：

10.1088/1674-4527/ad76eb

天文和天体物理学研究

中国科学院国家天文台

天文和天体物理学研究

CSTPCD

影响因子：0.406

ISSN：1674-4527

年,卷(期)：2024.24(10)