  Mesh generation remains a key technology in many areas where numerical
simulations are required. As numerical algorithms become more efficient and
computers become more powerful, the percentage of time devoted to mesh
generation becomes higher. In this paper, we present an improved structured
mesh generation method. The method formulates the meshing problem as a global
optimization problem related to a physics-informed neural network. The mesh is
obtained by intelligently solving the physical boundary-constrained partial
differential equations. To improve the prediction accuracy of the neural
network, we also introduce a novel auxiliary line strategy and an efficient
network model during meshing. The strategy first employs a priori auxiliary
lines to provide ground truth data and then uses these data to construct a loss
term to better constrain the convergence of the subsequent training. The
experimental results indicate that the proposed method is effective and robust.
It can accurately approximate the mapping (transformation) from the
computational domain to the physical domain and enable fast high-quality
structured mesh generation.
