  This paper focuses on addressing challenges posed by non-homogeneous
unstructured grids, commonly used in Computational Fluid Dynamics (CFD). Their
prevalence in CFD scenarios has motivated the exploration of innovative
approaches for generating reduced-order models. The core of our approach
centers on geometric deep learning, specifically the utilization of graph
convolutional network (GCN). The novel Autoencoder GCN architecture enhances
prediction accuracy by propagating information to distant nodes and emphasizing
influential points. This architecture, with GCN layers and encoding/decoding
modules, reduces dimensionality based on pressure-gradient values. The
autoencoder structure improves the network capability to identify key features,
contributing to a more robust and accurate predictive model. To validate the
proposed methodology, we analyzed two different test cases: wing-only model and
wing--body configuration. Precise reconstruction of steady-state distributed
quantities within a two-dimensional parametric space underscores the
reliability and versatility of the implemented approach.
