Publication Date:
2019-07-13
Description:
This chapter presents a neural-network-based technique that allows for the reconstruction of the global, time-varying distribution of some physical quantity Q, that has been sparsely sampled at various locations within the magnetosphere, and at different times. We begin with a general introduction to the problem of prediction and specification, and why it is important and difficult to achieve with existing methods. We then provide a basic introduction to neural networks, and describe our technique using the specific example of reconstructing the electron plasma density in the Earth's inner magnetosphere on the equatorial plane. We then show more advanced uses of the technique, including 3D reconstruction of the plasma density, specification of chorus and hiss waves, and energetic particle fluxes. We summarize and conclude with a general discussion of how machine learning techniques might be used to advance the state-of-the-art in space weather prediction, and insight discovery.
Keywords:
Lunar and Planetary Science and Exploration
Type:
GSFC-E-DAA-TN63232
,
Machine Learning Techniques for Space Weather; 279-300
Format:
text
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