30 citations · 58 across the 4 of their papers we have counts for
4 papers
Global geomagnetic perturbation forecasting using Deep Learning
Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi +6
Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere,…
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
Panagiotis Tigas, Téo Bloch, Vishal Upendran +6
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding model…
Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)
Ryan M. McGranaghan, Jack Ziegler, Téo Bloch +7
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…
Statistics of Solar Wind Electron Breakpoint Energies Using Machine Learning Techniques
Mayur R. Bakrania, I. Jonathan Rae, Andrew P. Walsh +4
Solar wind electron velocity distributions at 1 au consist of a thermal "core" population and two suprathermal populations: "halo" and "strahl". The core and halo are quasi-isotrop…