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

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dc.contributor.authorDorelli, John C.
dc.contributor.authorBard, Chris
dc.contributor.authorChen, Thomas Y.
dc.contributor.authorda Silva, Daniel
dc.contributor.authordos Santos, Luiz Fernando Guides
dc.contributor.authorIreland, Jack
dc.contributor.authorKirk, Michael
dc.contributor.authorMcGranaghan, Ryan
dc.contributor.authorNarock, Ayris
dc.contributor.authorNieves-Chinchilla, Teresa
dc.contributor.authorSamara, Marilia
dc.contributor.authorSarantos, Menelaos
dc.contributor.authorSchuck, Pete
dc.contributor.authorThompson, Barbara
dc.date.accessioned2023-11-30T18:49:39Z
dc.date.available2023-11-30T18:49:39Z
dc.date.issued2022-12-27
dc.descriptionHeliophysics 2050; virtual; May 3-7, 2021
dc.description.abstractTraditionally, 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.
dc.description.urihttps://arxiv.org/abs/2212.13328
dc.format.extent4 pages
dc.genrewhite papers
dc.genreconference papers and proceedings
dc.genrepreprints
dc.identifier.urihttps://doi.org/10.48550/arXiv.2212.13328
dc.identifier.urihttp://hdl.handle.net/11603/30950
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Goddard Planetary Heliophysics Institute (GPHI)
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis 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.
dc.rightsPublic Domain Mark 1.0en
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/
dc.titleDeep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0001-7537-3539

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