Space Debris and Near Earth Objects (NEO) might raise serious problems for the safeguard of our planet. Understanding their dynamics is of paramount importance. This Topical Collection aims at covering the major topics in the field, that include the orbit determination of NEO, their impact hazard analysis and possible strategies of deflection, the breakup and explosion analysis, the debris cloud evolution, the end-of-life analysis and possible disposal strategies of space debris.

+++Re-opened collection with new Guest Editor Valerio Carruba+++

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