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• W2806236786 abstract "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." @default.
• W2806236786 created "2018-06-13" @default.
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• W2806236786 date "2018-01-01" @default.
• W2806236786 modified "2023-10-15" @default.
• W2806236786 title "Artificial Neural Networks for Determining Magnetospheric Conditions" @default.
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• W2806236786 doi "https://doi.org/10.1016/b978-0-12-811788-0.00011-1" @default.
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