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\begin{document}

\title{A combined neural network- and physics-based approach for modeling
plasmasphere dynamics}



\author[1]{Irina S. Zhelavskaya}%
\author[2]{Nikita A Aseev}%
\author[3]{Yuri Shprits}%
\affil[1]{Helmholtz Centre Potsdam, GFZ German Research Centre For Geosciences, Potsdam, Germany}%
\affil[2]{GFZ German Research Centre for Geosciences}%
\affil[3]{GFZ Helmholtz Center Potsdam}%


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  \date{May 22, 2024}


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\begin{abstract}
In 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 decreases, as networks
inherently cannot learn from the limited number of examples. This
limitation can be overcome by employing physics-based modeling during
strong geomagnetic storms. Physics-based models show a stable
performance during periods of disturbed geomagnetic activity, if they
are correctly initialized and configured. In this study, we illustrate
how to combine the neural network- and physics-based models of the
plasmasphere in an optimal way by using the data assimilation Kalman
filtering. The proposed approach utilizes advantages of both neural
network- and physics-based modeling and produces global plasma density
reconstructions for both quiet and disturbed geomagnetic activity,
including extreme geomagnetic storms. We validate the models
quantitatively by comparing their output to the in-situ density
measurements from RBSP-A for an 18-month out-of-sample period from 30
June 2016 to 01 January 2018, and computing performance metrics. To
validate the global density reconstructions qualitatively, we compare
them to the IMAGE EUV images of the He+ particle distribution in the
Earth's plasmasphere for a number of events in the past, including the
Halloween storm in 2003.%
\end{abstract}%



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