An ANN-based advancing double-front method for automatic isotropic triangle generation

The advancing front method (AFM) is one of the widely used unstructured grid generation techniques. However, the efficiency is low due to the fact that only one cell is generated in the advancing procedure. 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. First, an initial isotropic triangular mesh is generated by the traditional mesh generation method as the training set of ANN. Second, the ADFM is presented in detail to improve the advancing efficiency, including the structure of the ANN to distinguish the different patterns in the advancing procedure. Finally, several typical cases are tested to validate the effectiveness. 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.