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Found 4 papers, 0 papers with code
Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification
For example, SYMHnet achieves a forecast skill score (FSS) of 0. 343 compared to the FSS of 0. 074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hour in advance) in a large storm (SYM-H = -393 nT) using 5-minute resolution data.
Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models
In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy.
Ensemble Learning for CME Arrival Time Prediction
In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth.
Deep Learning Based Reconstruction of Total Solar Irradiance
In this paper we propose a new method, called TSInet, to reconstruct total solar irradiance by deep learning for short and long periods of time that span beyond the physical models' data availability.
