The experimental results show that the ANN can accurately identify mesh generation patterns, and the mesh generation efficiency is 50% higher than that of the traditional single-front AFM. Several typical cases are tested to validate the effectiveness. The coordinates of the points contained in the dual fronts and the adjacent points are sent into the neural network as the grid generation environment to obtain the most possible mesh generation pattern, the corresponding methods are used to update the advancing front until the whole computational domain is covered by initial grids, and finally, some smoothing techniques are carried out to improve the quality initial grids. Third, after inputting the initial discrete boundary as initial fronts, the grid is generated from the shortest front and adjacent front. The preprocessed dataset is input into the ANN to train the network, then some typical patterns are obtained through learning. Second, an initial isotropic triangular mesh is generated by the traditional mesh generation method, and then an approach for automatic extraction of the training dataset is proposed. The mesh generation process in each pattern is discussed in detail. First, a variety of different patterns are extracted from the AFM mesh generation method and extended to the ADFM method. In this work, a novel automatic isotropic triangle generation technique is developed by introducing an artificial neural network (ANN) based advancing double-front method (ADFM) to improve the mesh generation efficiency. However, the efficiency is relatively low because only one cell is generated in the advancing procedure. The advancing front method (AFM) is one of the widely used unstructured grid generation techniques.

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