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A combined neural network- and physics-based approach for modeling plasmasphere dynamics

AbstractIn recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network-based models capture the large-scale dynamics of the plasmasphere, such as plume formation and erosion of the plasmasphere on the nightside. However, their performance depends strongly on the availability of training data. When the data coverage is limited or non-existent, as occurs during geomagnetic storms, the performance of NNs significantly de ...

Zhelavskaya, I.; Aseev, N.; . Y. Shprits, Y;

YEAR: 2021     DOI:

plasmasphere; plasma density; neural networks; data assimilation; Kalman Filter; Machine learning; Van Allen Probes


Empirical modeling of the plasmasphere dynamics using neural networks

We propose a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of October 1, 2012 - July 1, 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distributi ...

Zhelavskaya, Irina; Shprits, Yuri; c, Maria;

YEAR: 2017     DOI: 10.1002/2017JA024406

inner magnetosphere; Machine learning; Models; neural networks; plasmasphere; Van Allen Probes