Bibliography



Van Allen Probes Bibliography is from August 2012 through September 2021

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Found 3 entries in the Bibliography.


Showing entries from 1 through 3


2021

A dynamical model of equatorial magnetosonic waves in the inner magnetosphere: A machine learning approach

Abstract Equatorial magnetosonic waves, together with chorus and plasmaspheric hiss, play key roles in the dynamics of energetic electron fluxes in the magnetosphere. Numerical models, developed following a first principles approach, that are used to study the evolution of high energy electron fluxes are mainly based on quasilinear diffusion. The application of such numerical codes requires statistical models for the distribution of key magnetospheric wave modes to estimate the appropriate diffusion coefficients. These waves ...

Boynton, R.; Walker, S.; Aryan, H.; Hobara, Y.; Balikhin, M.;

Published by: Journal of Geophysical Research: Space Physics      Published on: 06/2021

YEAR: 2021     DOI: https://doi.org/10.1029/2020JA028439

magnetosonic waves; Machine learning; NARMAX; Van Allen Probes

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.; Shprits, Y;

Published by: Journal of Geophysical Research: Space Physics      Published on: 02/2021

YEAR: 2021     DOI: https://doi.org/10.1029/2020JA028077

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

2017

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;

Published by: Journal of Geophysical Research: Space Physics      Published on: 10/2017

YEAR: 2017     DOI: 10.1002/2017JA024406

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



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