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

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2212.13328.pdf (471.16 KB)

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https://arxiv.org/abs/2212.13328

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https://doi.org/10.48550/arXiv.2212.13328
 http://hdl.handle.net/11603/30950

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UMBC Goddard Planetary Heliophysics Institute (GPHI)
UMBC Faculty Collection

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https://orcid.org/0000-0001-7537-3539

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2022-12-27

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white papers
conference papers and proceedings
preprints
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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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.