Image classification of retrograde resonance in the planar circular restricted three-body problem
Authors (first, second and last of 7)
Collection
Machine Learning in Celestial Mechanics and Dynamical Astronomy
In the last two decades, machine learning has found application in a wide range of areas of science and scientific computing. In a number of cases this has led to new developments beyond the simple application of existing machine learning algorithms. More recently, machine learning has found interesting applications in the fields of astronomy, astrophysics and in space engineering, where machine learning for optimal control and trajectory design was applied to solve problems in mission analysis, space traffic management and on board autonomy.
This Topical Collection invites authors to submit high quality original contributions that address the many aspects of the use and development of machine learning in celestial mechanics and dynamical astronomy.
Authors are invited to submit papers on one or more of the following topics:
Coordinate transformation / Discovery of dynamical laws / Motion classification / Modelling of physical properties from data / Uncertainty quantification / Trajectory design / Orbit determination (ground-based and space-based) / Attitude determination
Editors
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Massimiliano Vasile
University of Strathclyde, UK
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Xiyun Hou
Nanjing University, China
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Roberto Furfaro
University of Arizona, USA
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Alessandra Celletti
University of Rome Tor Vergata
