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An artificial neural network, based on fuzzy ARTMAP, that is capable of learning the basic nonlinear dynamics of a turbulent velocity field is presented. The neural system is capable of generating a detailed multipoint time record with the same structural characteristics and basic statistics as those of the original instantaneous velocity field used for training. The good performance of the proposed architecture is demonstrated by the generation of synthetic two-dimensional velocity data at eight different positions along the homogeneous ͑spanwise͒ direction in the far region (x/Dϭ420) of a turbulent wake flow generated behind a cylinder at Reϭ1 200. The analysis of the synthetic velocity field, carried out with spectral techniques, POD and pattern recognition, reveals that the proposed neural system is capable of capturing the highly nonlinear dynamics of free turbulence and of reproducing the sequence of individual classes of relevant events present in turbulent wake flows. The trained neural system also yields patterns of the coherent structures embedded in the flow when presented with input data containing partial information of the instantaneous velocity maps of these events. In this way, the neural network is used as an expert system that helps in the structural interpretation of turbulence in a wake flow.

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