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arXiv · 2212.13328

Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory

John C. Dorelli·Chris Bard·Thomas Y. Chen·Daniel Da Silva·Luiz Fernando Guides dos Santos·Jack Ireland·Michael Kirk·Ryan McGranaghan·Ayris Narock·Teresa Nieves-Chinchilla·Marilia Samara·Menelaos Sarantos·Pete Schuck·Barbara Thompson
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Abstract

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

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astro-ph.IMastro-ph.SRcs.LGphysics.ao-phphysics.space-ph

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John C. Dorelli, Chris Bard, Thomas Y. Chen, Daniel Da Silva, Luiz Fernando Guides dos Santos, Jack Ireland, Michael Kirk, Ryan McGranaghan, Ayris Narock, Teresa Nieves-Chinchilla, Marilia Samara, Menelaos Sarantos, Pete Schuck, Barbara Thompson. 2022-12-27. Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory. https://arxiv.org/abs/2212.13328

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