Automated Generation of Hybrid Semi‐Structured Meshes via Boundary‐Informed Neural Networks

High‐quality anisotropic boundary‐layer meshes are crucial for numerical simulations, yet their automated generation for complex geometries remains a significant challenge. This paper presents AnisoMeshNet, a novel deep learning framework that automates the generation of hybrid semi‐structured meshes. At its core, AnisoMeshNet learns a smooth guiding vector field for anisotropic meshing by adopting a boundary‐informed neural network (BINN) philosophy. Instead of learning the field from scratch, our method first constructs a robust geometric prior—a boundary condition extension (BCE) field—using a K‐nearest neighbors (KNN) algorithm. The neural network is then trained to learn a smooth correction field that refines this prior, ensuring strict adherence to BCs through a weighted cosine similarity loss. The resulting composite vector field smoothly guides an advancing layer method (ALM) to generate orthogonality‐favored, quad‐dominant boundary‐layer meshes for complex 2D geometries, demonstrating a robust and automated alternative to traditional methods.