Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory
This addresses the gap between heliophysics data and theory for space weather prediction, but it is incremental as it builds on existing deep learning approaches without presenting new results.
The paper argues that deep learning can unify data analysis and theory to create more powerful space weather prediction models, calling for NASA investment in research and infrastructure to enable this advancement.
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.