Reconstructions of Jupiter's magnetic field using physics informed
  neural networks
Philip W. Livermore Leyuan Wu Longwei Chen Sjoerd A. L. de Ridder
Magnetic sounding using data collected from the Juno mission can be used to
provide constraints on Jupiter's interior. However, inwards continuation of
reconstructions assuming zero electrical conductivity and a representation in
spherical harmonics are limited by the enhancement of noise at small scales. In
this paper we describe new reconstructions of Jupiter's internal magnetic field
based on physics-informed neural networks and either the first 33 (PINN33) or
the first 50 (PINN50) of Juno's orbits. The method can resolve local
structures, and allows for weak ambient electrical currents. Compared with
other methods, our reconstructions of Jupiter's magnetic field both on and
above the surface are similar, and we achieve a similar fit to the Juno data.
However, our models are not hampered by noise at depth, and so offer a much
clearer picture of the interior structure. We estimate that the dynamo boundary
is at a fractional radius of 0.8. At this depth, the magnetic field is arranged
into longitudinal bands, and the great blue spot appears to be rooted in
neighbouring structures of oppositely signed flux.