24 citations · 51 across the 16 of their papers we have counts for
17 papers
Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning
Zhenduo Wang, Yasser Abduallah, Jason T. L. Wang +6
The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable…
Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network
Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang +6
Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and some flares are not. The associat…
Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model
Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang +6
We present a novel deep generative model, named GenMDI, to improve the temporal resolution of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michel…
Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model
Hongyang Zhang, Ju Jing, Jason T. L. Wang +6
We present a transformer model, named DeepHalo, to predict the occurrence of halo coronal mass ejections (CMEs). Our model takes as input an active region (AR) and a profile, where…
Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning
Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang +6
The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME…
A Deep Learning Approach to Operational Flare Forecasting
Yasser Abduallah, Jason T. L. Wang
Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. In this paper, we present a transf…
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network
Chunhui Xu, Jason T. L. Wang, Haimin Wang +4
Image super-resolution has been an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network (CNN) for solar image sup…
Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification
Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang +5
We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns f…
Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models
Khalid A. Alobaid, Yasser Abduallah, Jason T. L. Wang +5
Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two propert…
A Machine Learning Approach to Understanding the Physical Properties of Magnetic Flux Ropes in the Solar Wind at 1 AU
Hameedullah Farooki, Yasser Abduallah, Sung Jun Noh +5
Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether…
A Closer Look at Small-Scale Magnetic Flux Ropes in the Solar Wind at 1 AU: Results from Improved Automated Detection
Hameedullah Farooki, Sung Jun Noh, Jeongwoo Lee +7
Small-scale interplanetary magnetic flux ropes (SMFRs) are similar to ICMEs in magnetic structure, but are smaller and do not exhibit ICME plasma signatures. We present a computati…
A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data
Haodi Jiang, Qin Li, Zhihang Hu +8
Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have bee…
A Deep Learning Approach to Dst Index Prediction
Yasser Abduallah, Jason T. L. Wang, Prianka Bose +3
The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic st…
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…
Deep Learning Based Reconstruction of Total Solar Irradiance
Yasser Abduallah, Jason T. L. Wang, Yucong Shen +3
The Earth's primary source of energy is the radiant energy generated by the Sun, which is referred to as solar irradiance, or total solar irradiance (TSI) when all of the radiation…
DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction
Yasser Abduallah, Jason T. L. Wang, Yang Nie +2
Solar flare prediction plays an important role in understanding and forecasting space weather. The main goal of the Helioseismic and Magnetic Imager (HMI), one of the instruments o…
MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach
Yasser Abduallah, Turki Turki, Kevin Byron +3
Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and…