Modelling internal structure of differentiated asteroids via data-driven approach

This paper is devoted to an interdisciplinary method modelling the internal structure of differentiated asteroids via a data-driven approach called invertible neural networks (INNs). The model estimation of the internal structure can be generalized as an inverse problem of estimating internal parameters from a set of observations. Previous works (e.g. Park et al. 2014; Takahashi and Scheeres 2014) used the full gravity field data measures to derive the heterogeneous mass distribution. However, in our method, only the flight state of the spacecraft is adopted as the observation data. Since the internal parameters may not be uniquely determined, typical feedforward neural networks cannot simply be applied to such an inverse problem. The INNs adopted in this paper can ‘read’ the interior information from a flight trajectory of the spacecraft directly. The INNs are employed to establish the two-directional mapping between the group of physical parameters and the set of flight state observations of position and velocity. The INNs are trained in a bi-directional way using four losses. Finally, the performances of the trained networks are shown in both overfit and underfit situations where the internal structure of asteroids can be estimated by this INNs-based method accurately and effectively. The results also show that even when the degeneracy occurs, the true solution still falls inside the estimation distribution.