Dorelli, John C.Bard, ChrisChen, Thomas Y.da Silva, Danieldos Santos, Luiz Fernando GuidesIreland, JackKirk, MichaelMcGranaghan, RyanNarock, AyrisNieves-Chinchilla, TeresaSamara, MariliaSarantos, MenelaosSchuck, PeteThompson, Barbara2023-11-302023-11-302022-12-27https://doi.org/10.48550/arXiv.2212.13328http://hdl.handle.net/11603/30950Heliophysics 2050; virtual; May 3-7, 2021Traditionally, 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.4 pagesen-USThis 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.Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/Text