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

Although being studied since the XIX century, the main belt dynamics of asteroids between Mars and Jupiter still retains many unknowns as well as surprises. This Topical Collection aims at stimulating further studies on the main belt dynamics with particular reference to dynamical structure, population, families, and formation of asteroids, resonances with other celestial bodies, rotation, shape and structure of minor planets, as well as space missions to main belt objects.