  In the recent years, the domain of fast flow field prediction has been vastly
dominated by pixel-based convolutional neural networks. Yet, the recent advent
of graph convolutional neural networks (GCNNs) have attracted a considerable
attention in the computational fluid dynamics (CFD) community. In this
contribution, we proposed a GCNN structure as a surrogate model for laminar
flow prediction around 2D obstacles. Unlike traditional convolution on image
pixels, the graph convolution can be directly applied on body-fitted triangular
meshes, hence yielding an easy coupling with CFD solvers. The proposed GCNN
model is trained over a data set composed of CFD-computed laminar flows around
2,000 random 2D shapes. Accuracy levels are assessed on reconstructed velocity
and pressure fields around out-of-training obstacles, and are compared with
that of standard U-net architectures, especially in the boundary layer area.
