Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Dealing with the Sparse Data Challenge
This tackles the problem of limited data for space scientists studying magnetospheric processes, but it is incremental as it builds on existing AI approaches.
The paper addresses the challenge of understanding Earth's magnetosphere due to sparse in-situ observations from few probes, proposing new AI methods and missions to enhance mission science output.
In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.