  Recently, machine learning has been used to substitute parts of conventional
computational fluid dynamics, e.g. the cell-face reconstruction in
finite-volume solvers or the curvature computation in the Volume-of-Fluid (VOF)
method. The latter showed improvements in terms of accuracy for coarsely
resolved interfaces, however at the expense of convergence and symmetry. In
this work, a combined approach is proposed, adressing the aforementioned
shortcomings. We focus on interface reconstruction (IR) in the level-set
method, i.e. the computation of the volume fraction and apertures. The combined
model consists of a classification neural network, that chooses between the
conventional (linear) IR and the neural network IR depending on the local
interface resolution. The proposed approach improves accuracy for coarsely
resolved interfaces and recovers the conventional IR for high resolutions,
yielding first order overall convergence. Symmetry is preserved by mirroring
and rotating the input level-set grid and subsequently averaging the
predictions. The combined model is implemented into a CFD solver and
demonstrated for two-phase flows. Furthermore, we provide details of floating
point symmetric implementation and computational efficiency.
