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Title: Solar flare predictions with statistical learning Authors:  Yang Chen - University of Michigan (United States) [presenting]
Abstract: Over the space age, extensive knowledge has been accumulated about the regions of space surrounding the Earth and the Sun, and the governing physical processes controlling space weather in these regions. However, this knowledge has not been translated into an operational forecast capability. By combining our expertise in space weather modeling and data science/machine learning we can not only address the ``holy grail'' of space weather prediction and extend the forecast horizon from minutes to days, but also transition the results to space weather operations. The current space weather predictive capabilities are either short term and/or not accurate and reliable. We initiated a research program that will (hopefully) answer these questions using a unique combination of modeling, computation, and massive amounts of space weather data collected by satellites that will be used to train state-of-the-art machine learning algorithms.