Applications of Machine Learning in Spacecraft and Aerospace Systems

Dear Colleagues

Aerospace science and data-driven methods have been well integrated gradually. Prevalent data-driven methods such as deep learning (DL) and reinforcement learning (RL) solve many complex aerospace engineering problems that traditional methods are inefficient or impossible to solve. However, the main challenge of applying data-driven methods to aerospace lies in the attitude and orbital control, fault diagnosis, target recognition, situational awareness, mission planning, remote sensing data analysis, in-orbit service, and human–computer interaction in aerospace.

This Special Issue aims to gather a collection of the latest studies in solving aerospace problems with data-driven methods from theoretical or practical perspectives. We welcome new or improved methods for modeling, controlling, learning, optimization, and decision support problems. Particular interest is also paid to the applications of fault diagnosis, life prediction, functional reconstruction, and healthy operation for spacecraft. We invite authors to submit research papers and/or review papers that fit this purpose.

Prof. Dr. Ming Liu
Dr. Chengxi Zhang
Dr. Zhiqiang Ma
Guest Editors

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