  In machine learning for fluid mechanics, fully-connected neural network (FNN)
only uses the local features for modelling, while the convolutional neural
network (CNN) cannot be applied to data on structured/unstructured mesh. In
order to overcome the limitations of FNN and CNN, the unstructured
convolutional neural network (UCNN) is proposed, which aggregates and
effectively exploits the features of neighbour nodes through the weight
function. Adjoint vector modelling is taken as the task to study the
performance of UCNN. The mapping function from flow-field features to adjoint
vector is constructed through efficient parallel implementation on GPU. The
modelling capability of UCNN is compared with that of FNN on validation set and
in aerodynamic shape optimization at test case. The influence of mesh changing
on the modelling capability of UCNN is further studied. The results indicate
that UCNN is more accurate in modelling process.
