Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Dealing with the Sparse Data Challenge

TL;DR

This paper discusses how AI techniques like machine learning and data assimilation can address the challenge of sparse in-situ observational data in Earth's magnetosphere, improving understanding of its structure and dynamics.

Contribution

It proposes the development of AI-enabled methods and missions to enhance data analysis and scientific output in the context of limited in-situ observations.

Findings

AI methods can improve interpretation of sparse data

AI-enabled missions can fill observational gaps

Enhanced understanding of magnetospheric processes

Abstract

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.