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Space Weather: A Multi-Disciplinary Approach
The study of space weather has traditionally been carried out using standard techniques and tools found in space physics such as time series correlational analyses. These techniques, although having the advantage of being fast and simple, are sometimes not adequate or complete because the Sun-Earth system is a complex nonlinear system.
On the other hand, researchers in the fields of mathematics, information science, computer science, machine learning, data mining, have developed, over the last several decades, tools that can handle complex nonlinear systems and are eager to apply these new tools to new difficult problems.
The aim of this workshop is to bring together researchers from space weather, space physics, mathematics, computer science, information science, machine learning, data mining, etc. to foster symbiosis and cross-fertilization across the fields.
The topics that will be discussed include:
-- machine learning for Space Weather
-- information theory for Sun-Earth system
-- pattern recognition and deep learning of solar images
-- data mining in space physics