论文标题
人工智能增强了原地观察的任务科学输出：处理稀疏数据挑战
Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Dealing with the Sparse Data Challenge
论文作者
论文摘要
在地球的磁层中，在任何给定时间，都有低地球轨道进行原位观测值以外的十几个专用探针。结果，我们不了解其全球结构和进化，其主要活性过程的机制，磁性风暴和化型。新的人工智能（AI）方法，包括机器学习，数据挖掘和数据同化，以及新的AI-ai-ai-ables任务，以应对这一稀疏数据挑战。
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.
