159 citations · 255 across the 5 of their papers we have counts for
5 papers
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network
Yasser Abduallah, Vania K. Jordanova, Hao Liu +3
Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general. In this paper we propose…
Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning
Haodi Jiang, Jiasheng Wang, Chang Liu +4
Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments. Here we propo…
Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network
Hao Liu, Yan Xu, Jiasheng Wang +4
We propose a new machine learning approach to Stokes inversion based on a convolutional neural network (CNN) and the Milne-Eddington (ME) method. The Stokes measurements used in th…
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
Hao Liu, Chang Liu, Jason T. L. Wang +1
We present two recurrent neural networks (RNNs), one based on gated recurrent units and the other based on long short-term memory, for predicting whether an active region (AR) that…
Predicting Solar Flares Using a Long Short-Term Memory Network
Hao Liu, Chang Liu, Jason T. L. Wang +1
We present a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a gamma-class flare within the next 24 hours. We consider three gamma…