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

• URL: http://arxiv.org/abs/2212.13328v1
• Date: Tue, 27 Dec 2022 00:30:39 GMT
• Title: Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory
• Authors: 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
• Abstract summary: Deep learning technology will produce a new class of predictively powerful space weather models. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.
• Score: 3.5597536699796795
• License: http://creativecommons.org/licenses/by/4.0/
• 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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