85 citations · 170 across the 18 of their papers we have counts for
18 papers
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
Sathvik Soman, Jason T. L. Wang, Haimin Wang +1
We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive days during significant solar f…
Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations
Zhaoxin Yan, Jason T. L. Wang, Haimin Wang +5
Understanding and forecasting the geoeffectiveness of a coronal mass ejection (CME) is crucial for protecting infrastructure in the near-Earth space environment and on Earth. In th…
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…
Out-of-Sample Validation of MagNet
Aryiadna Yesmanchyk, Yan Xu, Jason T. L. Wang +3
Machine learning is starting to be used in almost every industry and academic research, and solar physics is no exception. A newly developed machine learning model named MagNet hel…
Reconstruction of Solar EUV Irradiance Using CaII K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Haodi Jiang, Qin Li, Jason T. L. Wang +2
Solar extreme ultraviolet (EUV) irradiance plays a crucial role in heating the Earth's ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time sc…
An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions
Huseyin Cavus, Jason T. L. Wang, Teja P. S. Singampalli +4
Solar flares are defined as outbursts on the surface of the Sun. They occur when energy accumulated in magnetic fields enclosing solar active regions (ARs) is abruptly expelled. So…
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…
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…
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…
Extreme Red-wing Enhancements of UV Lines During the 2022 March 30 X1.3 Solar Flare
Yan Xu, Graham S. Kerr, Vanessa Polito +3
Here we present the study of a compact emission source during an X1.3 flare on 2022-March-30. Within a ~s period (17:34:48 UT to 17:35:29 UT), IRIS observations show spectr…
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…
Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Haodi Jiang, Qin Li, Yan Xu +5
Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics. In this paper, we present a new deep learning method, named Stacked Deep N…
Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods
Hewei Zhang, Qin Li, Yanxing Yang +4
Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that can…
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
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…
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…