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

TL;DR

This paper discusses how deep learning can unify data analysis and theoretical modeling in space weather prediction, promising more accurate models by combining physical insights with data-driven approaches.

Contribution

It highlights the potential of deep learning to bridge heliophysics data and theory, advocating for NASA's investment in this interdisciplinary approach.

Findings

Deep learning can integrate data and theory for space weather prediction.

Unified models enhance predictive power over traditional methods.

Call for increased investment in heliophysics research infrastructure.

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.