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.
@article{arxiv.2212.13328,
title = {Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory},
author = {John C. Dorelli and Chris Bard and Thomas Y. Chen and Daniel Da Silva and Luiz Fernando Guides dos Santos and Jack Ireland and Michael Kirk and Ryan McGranaghan and Ayris Narock and Teresa Nieves-Chinchilla and Marilia Samara and Menelaos Sarantos and Pete Schuck and Barbara Thompson},
journal= {arXiv preprint arXiv:2212.13328},
year = {2022}
}