论文标题
对太空天气预测的深度学习：弥合热物理数据与理论之间的差距
Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory
论文作者
论文摘要
传统上，数据分析和理论被视为单独的学科，每个学科都融入了根本不同类型的模型中。现代深度学习技术开始统一这两个学科，并将生成一类新的预测空间天气模型，以结合数据和理论获得的物理见解。我们呼吁NASA投资于Heliophysics社区利用这些进步所需的研究和基础设施。
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.
