Multi-objective evolutionary recurrent neural network ensemble for prediction of computational fluid dynamic simulations

Using a surrogate model to evaluate the expensive fitness of candidate solutions in an evolutionary algorithm can significantly reduce the overall computational cost of optimization tasks. In this paper we present a recurrent neural network ensemble that is used as a surrogate for the long-term prediction of computational fluid dynamic simulations. A hybrid multi-objective evolutionary algorithm that trains and optimizes the structure of the recurrent neural networks is introduced. Selection and combination of individual prediction models in the Pareto set of solutions is used to create the ensemble of predictors. Five selection methods are tested on six data sets and the accuracy of the ensembles is compared to the converged computational fluid dynamic data, as well as to the delta change between two flow conditions. Intermediate computational fluid dynamic data is used for training and the method presented can produce accurate and stable results using a third of the intermediate data needed for convergence.