  In a porous medium featuring heterogeneous permeabilities, a wide range of
fluid velocities may be recorded, so that significant inertial and frictional
effects may arise in high-speed regions. In such parts, the link between
pressure gradient and velocity is typically made via Darcy's law, which may
fail to account for these effects; instead, the Darcy Forchheimer law, which
introduces a nonlinear term, may be more adequate. Applying the Darcy
Forchheimer law globally in the domain is very costly numerically and, rather,
should only be done where strictly necessary. The question of finding a prori
the subdomain where to restrict the use of the Darcy Forchheimer law was
recently answered in FP23 by using an adaptive model: given a threshold on the
flow velocity, the model locally selects the more appropriate law as it is
being solved. At the end of the resolution, each mesh cell is flagged as being
in the Darcy or Darcy Forchheimer subdomain. Still, this model is nonlinear
itself and thus relatively expensive to run. In this paper, to accelerate the
subdivision of the domain into low and high speed regions, we instead exploit
the adaptive model from FP23 to generate partitioning data given an array of
different input parameters, such as boundary conditions and inertial
coefficients, and then train neural networks on these data classifying each
mesh cell as Darcy or not. Two test cases are studied to illustrate the
results, where cost functions, parity plots, precision-recall plots and
receiver operating characteristic curves are analyzed.
