  Many scientific and engineering processes produce spatially unstructured
data. However, most data-driven models require a feature matrix that enforces
both a set number and order of features for each sample. They thus cannot be
easily constructed for an unstructured dataset. Therefore, a graph based
data-driven model to perform inference on fields defined on an unstructured
mesh, using a Graph Convolutional Neural Network (GCNN) is presented. The
ability of the method to predict global properties from spatially irregular
measurements with high accuracy is demonstrated by predicting the drag force
associated with laminar flow around airfoils from scattered velocity
measurements. The network can infer from field samples at different
resolutions, and is invariant to the order in which the measurements within
each sample are presented. The GCNN method, using inductive convolutional
layers and adaptive pooling, is able to predict this quantity with a validation
$R^{2}$ above 0.98, and a Normalized Mean Squared Error below 0.01, without
relying on spatial structure.
