  We consider non-spherical rigid body particles in an incompressible fluid in
the regime where the particles are too large to assume that they are simply
transported with the fluid without back-coupling and where the particles are
also too small to make fully resolved direct numerical simulations feasible.
Unfitted finite element methods with ghost-penalty stabilisation are well
suited to fluid-structure-interaction problems as posed by this setting, due to
the flexible and accurate geometry handling and for allowing topology changes
in the geometry. In the computationally under resolved setting posed here,
accurate computations of the forces by their boundary integral formulation are
not viable. Furthermore, analytical laws are not available due to the shape of
the particles. However, accurate values of the forces are essential for
realistic motion of the particles. To obtain these forces accurately, we train
an artificial deep neural network using data from prototypical resolved
simulations. This network is then able to predict the force values based on
information which can be obtained accurately in an under-resolved setting. As a
result, we obtain forces on very coarse and under-resolved meshes which are on
average an order of magnitude more accurate compared to the direct
boundary-integral computation from the Navier-Stokes solution, leading to solid
motion comparable to that obtained on highly resolved meshes that would
substantially increase the simulation costs.
