Solar Wind

Solar wind, the continuous stream of charged particles emanating from the Sun, is a key driver of space weather, impacting Earth's magnetosphere and potentially disrupting technological infrastructure. Current research heavily utilizes machine learning, particularly deep learning architectures like recurrent neural networks (RNNs) and convolutional neural networks (CNNs), to improve the accuracy and speed of solar wind forecasting, focusing on predicting geomagnetic indices (e.g., Dst, SYM-H) and the occurrence of events like coronal mass ejections (CMEs) and solar energetic particle (SEP) events. These advancements are crucial for mitigating the risks associated with space weather, enabling more accurate predictions and timely warnings to protect satellites, power grids, and other critical systems. Furthermore, research is exploring the use of Bayesian methods to quantify uncertainties in predictions, enhancing the reliability of space weather forecasts.

Papers

October 28, 2024

Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of Concept
Grégoire Francisco, Francesco Pio Ramunno, Manolis K. Georgoulis, João Fernandes, Teresa Barata, Dario Del Moro
Diffusion Explainer Denoising Process Diffusion Probabilistic Model High Impact Concept Space Weather Solar Wind Complex Generative Solar Corona

July 9, 2024

TriQXNet: Forecasting Dst Index from Solar Wind Data Using an Interpretable Parallel Classical-Quantum Framework with Uncertainty Quantification
Md Abrar Jahin, M. F. Mridha, Zeyar Aung, Nilanjan Dey, R. Simon Sherratt
Uncertainty Quantification Space Weather Geomagnetic Storm Solar Wind Index Forecasting

March 14, 2024

Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches
Sabrina Guastavino, Katsiaryna Bahamazava, Emma Perracchione, Fabiana Camattari, Gianluca Audone, Daniele Telloni, Roberto Susino, Gianalfredo Nicolini, Silvano Fineschi, Michele Piana, Anna Maria Massone
Event Forecasting Geomagnetic Storm Solar Wind Magnetic Perturbation Symptom H Index

February 27, 2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification
Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing
Human Prediction Uncertainty Quantification Bayesian Deep Learning Solar Wind Symptom H Index

October 23, 2023

MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach
Kimberly Moreland, Maher Dayeh, Hazel M. Bain, Subhamoy Chatterjee, Andres Munoz-Jaramillo, Samuel Hart
New Machine Rare Event Solar Wind Solar Energetic Particle

June 20, 2023

Neural Astrophysical Wind Models
Dustin D. Nguyen
Neural Ordinary Differential Equation Flow Field Solar Wind

April 4, 2023

Heating and dynamics of the Solar atmosphere
Vishal Upendran
Solar Wind Solar Corona

April 3, 2023

Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms
Rong Lin, Zhekai Luo, Jiansen He, Lun Xie, Chuanpeng Hou, Shuwei Chen
Convolutional Neural Network Human Prediction Potential Field Solar Wind

February 13, 2023

Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA
Federico Sabbatini, Catia Grimani
Machine Learning Model Ensemble Learning Ensemble Model Space Weather Solar Wind Cosmic Ray

October 30, 2022

Regimes of charged particle dynamics in current sheets: the machine learning approach
Alexander Lukin, Anton Artemyev, Dmitri Vainchtein, Anatoli Petrukovich
New Machine Constructive Approach Chaotic Regime Magnetic Field Particle Dynamic Solar Wind

May 12, 2022

Global geomagnetic perturbation forecasting using Deep Learning
Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Teo Bloch, Mark C. M. Cheung, Siddha Ganju, Asti Bhatt, Ryan M. McGranaghan, Yarin Gal
Deep Learning Geomagnetic Storm Solar Wind Magnetic Perturbation

May 7, 2022

Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data
Hannah T. Rüdisser, Andreas Windisch, Ute V. Amerstorfer, Christian Möstl, Tanja Amerstorfer, Rachel L. Bailey, Martin A. Reiss
Automatic Detection Situ Data Solar Wind Coronal Mass Ejection

February 3, 2022

Machine Learning Solar Wind Driving Magnetospheric Convection in Tail Lobes
Xin Cao, Jasper S. Halekas, Stein Haaland, Suranga Ruhunusiri, Karl-Heinz Glassmeier
Solar Wind

Solar Wind

Papers

Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of Concept

TriQXNet: Forecasting Dst Index from Solar Wind Data Using an Interpretable Parallel Classical-Quantum Framework with Uncertainty Quantification

Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification

MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach

Neural Astrophysical Wind Models

Heating and dynamics of the Solar atmosphere

Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms

Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA

Regimes of charged particle dynamics in current sheets: the machine learning approach

Global geomagnetic perturbation forecasting using Deep Learning

Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data

Machine Learning Solar Wind Driving Magnetospheric Convection in Tail Lobes