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Found 5 papers, 2 papers with code
Global geomagnetic perturbation forecasting using Deep Learning
The model summarizes 2 hours of solar wind measurement using a Gated Recurrent Unit, and generates forecasts of coefficients which are folded with a spherical harmonic basis to enable global forecasts.
Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes
We develop multiple Deep Learning (DL) models that advance the state-of-the-art predictions of the global auroral particle precipitation.
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge.
Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects
We also provide relevant critical risk indicators (CRIs) across diverse domains that may influence electric power grid risks, including climate, ecology, hydrology, finance, space weather, and agriculture.
Applications
Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)
We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning (ML) tools to gain utility from those data.
