arrow
返回

Real-time solar coronal modelling

delete2023-10-17
delete0
PRE
AI
M
M. S. Wheatland *
DOI:10.1038/s41550-023-02085-8delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Physics-informed neural networks allow the construction of state-of-the-art models of magnetic fields in active regions on the Sun in real time, enabling rapid investigation of the source regions for space weather.
Keyword:
MAGNETIC-FIELD

期刊

Nature Astronomy 封面图
Nature Astronomy
IF:
14.3
论文数:
2.8K
被引数:
1.3W

机构

U
University of Sydney
学者数:
6.5W
论文数: 6.2W
被引数: 90
引用论文

引用论文

EVOLUTION OF MAGNETIC FIELD AND ENERGY IN A MAJOR ERUPTIVE ACTIVE REGION BASED ON SDO/HMI OBSERVATION
err2012-03-09
err317
errOAAI
errSun, Xudong; Hoeksema, J. Todd; Liu, Yang; Wiegelmann, Thomas; Hayashi, Keiji; Chen, Qingrong; Thalmann, Julia
err分享
err收藏
Physics-informed neural networks (PINNs) for fluid mechanics: a review用于流体力学的物理信息神经网络 (PINNs): 综述
err2022-01-23
err820
PREAI
errCai, Shengze; Mao, Zhiping; Wang, Zhicheng; Yin, Minglang; Karniadakis, George Em
err分享
err收藏
err分享
err收藏
Cu-Ni Thin Films Electrodeposited on Si: Composition and Current Efficiency电沉积在Si上的cu-ni薄膜: 组成和电流效率
err2001-09-01
err0
PREAI
errM.L. Sartorelli; A.Q. Schervenski; R.G. Delatorre; P. Klauss; A.M. Maliska; A.A. Pasa
err分享
err收藏
Probing the solar coronal magnetic field with physics-informed neural networks
err2023-07-13
err25
errOAAI
errJarolim, R.; Thalmann, J. K.; Veronig, A. M.; Podladchikova, T.
err分享
err收藏
没有更多内容