Prediction of Dynamic Plasmapause Location Using a Neural Network

Guo, Deyu; Fu, Song; Xiang, Zheng; Ni, Binbin; Guo, Yingjie; Feng, Maoyuan; Guo, Jianguang; Hu, Zejun; Gu, Xudong; Zhu, Jianan; Cao, Xing; Wang, Qi

doi:10.1029/2020sw002622

Cited by 12 publications

(13 citation statements)

References 69 publications

(116 reference statements)

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“…[2017]; Guo et al. [2021]; Malaspina et al. [2020]), hence the change in the distributions slopes between L = 3 and L = 3.5 should be attributed to a statistically averaged plasmapause location.…”

Section: Resultsmentioning

confidence: 99%

Data‐Driven Discovery of Fokker‐Planck Equation for the Earth's Radiation Belts Electrons Using Physics‐Informed Neural Networks

et al. 2022

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where f is the particles' PSD, Φ is the third adiabatic invariant (magnetic flux enclosed by a drift shell), t is time, and Equation 1 is understood to be valid for constant values of first and second adiabatic invariants. The drift and diffusion coefficients (C Φ and D Φ , respectively) have the physical meaning of mean displacement and mean square displacement per unit time. Typically, Equation 1 is further simplified by assuming a simple relationship

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Section: Resultsmentioning

confidence: 99%

Data‐Driven Discovery of Fokker‐Planck Equation for the Earth's Radiation Belts Electrons Using Physics‐Informed Neural Networks

et al. 2022

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“…The results are shown in Figure 4. For each color-coded set in Figure 4, the solid line represents the average CCs of the prediction results of every 10 trained models, the shaded region displays the variation range of the CCs, and the upper and lower boundaries denote the sum and the difference of the mean and standard deviation, respectively (Guo et al 2021).…”

Section: Resultsmentioning

confidence: 99%

Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method

Huang

et al. 2022

ApJS

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The prediction of the Dst index is an important subject in space weather. It has significant progress with the prevalent applications of neural networks. The selection of input parameters is critical for the prediction model of the Dst index or other space-weather models. In this study, we perform a layer-wise relevance propagation (LRP) method to select the main parameters for the prediction of the Dst index and understand the physical interpretability of neural networks for the first time. Taking an hourly Dst index and 10 types of solar wind parameters as the inputs, we utilize a long short-term memory network to predict the Dst index and present the LRP method to analyze the dependence of the Dst index on these parameters. LRP defines the relevance score for each input, and a higher relevance score indicates that the corresponding input parameter contributes more to the output. The results show that Dst, E y , B z , and V are the main control parameters for Dst index prediction. In order to verify the LRP method, we design two more supplementary experiments for further confirmation. These results confirm that the LRP method can reduce the initial dimension of neural network input at the cost of minimum information loss and contribute to the understanding of physical processes in space weather.

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“…Background electron density from EMIFSIS L4 data product is shown in Figure 1b. The L‐shells of satellite positions (black line, McIlwain's L‐shell parameter, computed using the OP77Q external field and the IGRF internal field) and plasmapause locations (orange line, calculated using a neutral network model in Guo et al., 2021) are shown in Figure 1c. The satellite positions are always higher than plasmapause positions, suggesting that the satellite was out of plasmapause during this period.…”

Section: Data Sets and Event Selectionmentioning

confidence: 99%

“…(c) The L values of satellite positions and the plasmapause locations calculated by the Guo et al. (2021) model. (d and e) Frequency‐time spectrogram of electric and magnetic field spectral intensity, the black lines represent f ce , 0.5 f ce and 0.1 f ce from the top to bottom, respectively.…”

Section: Data Sets and Event Selectionmentioning

confidence: 99%

Bounce Resonance Scattering of Radiation Belt Energetic Electrons by Extremely Low‐Frequency Chorus Waves

Guo

Xiang

et al. 2021

Geophysical Research Letters

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Prediction of Dynamic Plasmapause Location Using a Neural Network

Cited by 12 publications

References 69 publications

Data‐Driven Discovery of Fokker‐Planck Equation for the Earth's Radiation Belts Electrons Using Physics‐Informed Neural Networks

Data‐Driven Discovery of Fokker‐Planck Equation for the Earth's Radiation Belts Electrons Using Physics‐Informed Neural Networks

Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method

Bounce Resonance Scattering of Radiation Belt Energetic Electrons by Extremely Low‐Frequency Chorus Waves

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