  LISA Pathfinder is a science and technology demonstrator of the European
Space Agency within the framework of its LISA mission, which aims to be the
first space-borne gravitational wave observatory. The payload of LISA
Pathfinder is the so-called LISA Technology Package, which is designed to
measure relative accelerations between two test masses in nominal free fall.
Its disturbances are monitored and dealt by the diagnostics subsystem. This
subsystem consists of several modules, and one of these is the magnetic
diagnostics system, which includes a set of four tri-axial fluxgate
magnetometers, intended to measure with high precision the magnetic field at
the positions of the test masses. However, since the magnetometers are located
far from the positions of the test masses, the magnetic field at their
positions must be interpolated. It has been recently shown that because there
are not enough magnetic channels, classical interpolation methods fail to
derive reliable measurements at the positions of the test masses, while neural
network interpolation can provide the required measurements at the desired
accuracy. In this paper we expand these studies and we assess the reliability
and robustness of the neural network interpolation scheme for variations of the
locations and possible offsets of the magnetometers, as well as for changes in
environmental conditions. We find that neural networks are robust enough to
derive accurate measurements of the magnetic field at the positions of the test
masses in most circumstances.
