Investigation of the Robustness of Neural Density Fields
Jonas Schuhmacher Fabio Gratl Dario Izzo Pablo Gómez
Recent advances in modeling density distributions, so-called neural density
fields, can accurately describe the density distribution of celestial bodies
without, e.g., requiring a shape model - properties of great advantage when
designing trajectories close to these bodies. Previous work introduced this
approach, but several open questions remained. This work investigates neural
density fields and their relative errors in the context of robustness to
external factors like noise or constraints during training, like the maximal
available gravity signal strength due to a certain distance exemplified for 433
Eros and 67P/Churyumov-Gerasimenko. It is found that both models trained on a
polyhedral and mascon ground truth perform similarly, indicating that the
ground truth is not the accuracy bottleneck. The impact of solar radiation
pressure on a typical probe affects training neglectable, with the relative
error being of the same magnitude as without noise. However, limiting the
precision of measurement data by applying Gaussian noise hurts the obtainable
precision. Further, pretraining is shown as practical in order to speed up
network training. Hence, this work demonstrates that training neural networks
for the gravity inversion problem is appropriate as long as the gravity signal
is distinguishable from noise.
  Code and results are available at https://github.com/gomezzz/geodesyNets