Fluid flow simulation

A neural network to simulate aerodynamic performance of a technical object. Training the neural network uses a plurality of sets of encodings of pre-computed computational fluid dynamics (CFD) outputs generated using inputs comprising: the geometry of training technical objects, spatial location of input nodes of the neural network as node attributes, a relationship between the geometry of the training technical objects and the neural network node locations, associated boundary conditions, operating conditions, and computed outputs comprising flow fields and aerodynamic performance parameters. The training process uses a loss function that evaluates an error between the neural network output and the pre-computed CFD outputs. The trained network generates a predicted aerodynamic performance of a technical object. The relationship between the geometry of the training technical objects and the neural network node locations comprises a vector with at least two parameters. The embodiments use a Graph Neural Network to model CFD simulations of fluid flow over an object, where the object geometry, input nodes of the GNN and a spatial relationship between input nodes of the GNN and object geometry are all given as inputs to the GNN. This allows cell-centroid based graph node meshes 2(b) to be used with GNNs for CFD simulation.