30 citations · 63 across the 12 of their papers we have counts for
12 papers
Reimagining Heliophysics: A bold new vision for the next decade and beyond
Ian J. Cohen, Dan Baker, Jacob Bortnik +25
The field of Heliophysics has a branding problem. We need an answer to the question: ``What is Heliophysics\?'', the answer to which should clearly and succinctly defines our scien…
Complexity Heliophysics: A lived and living history of systems and complexity science in Heliophysics
Ryan M. McGranaghan
This review examines complexity science in Heliophysics, describing it not as a discipline, but as a paradigm. In the context of Heliophysics, complexity science is the study of a…
Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory
John C. Dorelli, Chris Bard, Thomas Y. Chen +11
Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is begi…
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,…
Revisiting the Solar Research Cyberinfrastructure Needs: A White Paper of Findings and Recommendations
Gelu Nita, Azim Ahmadzadeh, Serena Criscuoli +15
Solar and Heliosphere physics are areas of remarkable data-driven discoveries. Recent advances in high-cadence, high-resolution multiwavelength observations, growing amounts of dat…
Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes
Jack Ziegler, Ryan M. Mcgranaghan
We develop multiple Deep Learning (DL) models that advance the state-of-the-art predictions of the global auroral particle precipitation. We use observations from low Earth orbitin…