79 citations · 208 across the 6 of their papers we have counts for
6 papers
Machine Learning Interpretability of Outer Radiation Belt Enhancement \& Depletion Events
Donglai Ma, Jacob Bortnik, Qianli Ma +2
We investigate the response of outer radiation belt electron fluxes to different solar wind and geomagnetic indices using an interpretable machine learning method. We reconstruct t…
Opening the Black Box of the Radiation Belt Machine Learning Model
Donglai Ma, Jacob Bortnik, Xiangning Chu +3
Many Machine Learning (ML) systems, especially neural networks, are fundamentally regarded as black boxes since it is difficult to grasp how they function once they have been train…
Relativistic electron flux model in the outer radiation belt using a neural network approach
Xiangning Chu, Donglai Ma, Jacob Bortnik +10
We present a machine-learning-based model of relativistic electron fluxes >1.8 MeV using a neural network approach in the Earth's outer radiation belt. The Outer RadIation belt Ele…
Modeling the dynamic variability of sub-relativistic outer radiation belt electron fluxes using machine learning
Donglai Ma, Xiangning Chu, Jacob Bortnik +6
We present a set of neural network models that reproduce the dynamics of electron fluxes in the range of 50 keV 1 MeV in the outer radiation belt. The Outer Radiation belt E…
Identifying the magnetospheric driver of STEVE
Xiangning Chu, David Malaspina, Bea Gallardo-Lacourt +18
For the first time, we identify the magnetospheric driver of STEVE, east-west aligned narrow emissions in the subauroral region. In the ionosphere, STEVE is associated with subauro…
On the generation of probabilistic forecasts from deterministic models
Enrico Camporeale, Xiangning Chu, Oleksiy Agapitov +1
Most of the methods that produce space weather forecasts are based on deterministic models. In order to generate a probabilistic forecast, a model needs to be run several times sam…