The numerous recent breakthroughs in machine learning make imperative to carefully
ponder how the scientific community can benefit from a technology that, although not …

This is a tutorial review on systems theory and its applications to space plasma physics and,
more broadly, on geophysics. With its basis on the state representation of a plasma the …

This paper aims to develop the long short-term memory (LSTM) network modelling strategy
based on deep learning principles, tailored for the very short-term, near-real-time global …

A multilayer feed‐forward neural network model has been developed to forecast> 2 MeV
electron flux at geosynchronous orbit. The model uses as input 10 consecutive days of …

A goal of predictive models of the space radiation environment is to provide advanced
knowledge of significant variations in the highly energetic particle populations that form the …

This work designs a new model called PreMevE to predict storm time distributions of
relativistic electrons within Earth's outer radiation belt. This model takes advantage of the …

There are hundreds of satellites operating at the geosynchronous (GEO) orbit where
relativistic electrons can cause severe damage. Thus, predicting relativistic electron fluxes is …

We present detailed analysis of the global relativistic electron dynamics as measured by
total radiation belt content (RBC) during coronal mass ejection (CME) and corotating …

Data assimilation aims to smoothly blend incomplete and inaccurate observational data with
dynamical information from a physical model, and has become an increasingly important …

Simultaneously cycling space weather parameters may show high correlations even if there
is no immediate relationship between them. We successfully remove diurnal cycles using …