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Found 20 papers, 6 papers with code
Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning
The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently.
Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges
With recent missions such as advanced space-based observatories like the Solar Dynamics Observatory (SDO) and Parker Solar Probe, and ground-based telescopes like the Daniel K. Inouye Solar Telescope (DKIST), the volume, velocity, and variety of data have made solar physics enter a transformative era as solar physics big data (SPBD).
Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field Model
The goal of this study is to accelerate coronal magnetic field simulation using deep learning, specifically, the Fourier Neural Operator (FNO).
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network
Image super-resolution has been an important subject in image processing and recognition.
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.
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee
This paper focuses on two model compression techniques: low-rank approximation and weight pruning in neural networks, which are very popular nowadays.
A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data
We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data.
Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics.
Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods
In addition, we sort the importance of SHARP parameters by Borda Count method calculated from the ranks that are rendered by 9 different machine learning methods.
A Deep Learning Approach to Dst Index Prediction
To our knowledge, this is the first time that Bayesian deep learning has been used for Dst index forecasting.
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network
Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general.
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.
Tracing Halpha Fibrils through Bayesian Deep Learning
Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations.
DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction
Solar flare prediction plays an important role in understanding and forecasting space weather.
Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning
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.
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.
Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network
By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS.
Solar and Stellar Astrophysics
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
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 produces an M- or X-class flare will also produce a coronal mass ejection (CME).
Predicting Solar Flares Using a Long Short-Term Memory Network
The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples.
