What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives

Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG. To augment reader accessibility, a dedicated IMG project page is available at \url{https://github.com/xzb030/IMG_Survey}

Anisotropic Quadrilateral Mesh Optimization

In order to determine the validity and the quality of meshes, mesh optimization methods have been formulated with quality measures. The basic idea of mesh optimization is to relocate the vertices to obtain a valid mesh (untangling) or improve the mesh quality (smoothing), or both. We will look at a new algebraic way of calculating quality measure on quadrilateral meshes, based on triangular meshes in 2D as well as new optimization methods for simultaneous untangling and smoothing for severely deformed meshes. An innovative anisotropic diffusion method will be introduced for consideration of inner boundary deformation movements for quadrangle meshes in 2D

Interior boundary-aligned unstructured grid generation and cell-centered versus vertex-centered CVD-MPFA performance

Grid generation for reservoir simulation must honor classical key constraints and ensure boundary alignment such that control-volume boundaries are aligned with geological features including layers, shale barriers, fractures, faults, pinch-outs, and multilateral wells. Novel unstructured grid generation methods are proposed that automate control-volume and/or control point boundary alignment and yield perpendicular-bisector (PEBI) meshes both with respect to primal and dual (essentially PEBI) cells. In order to honor geological features in the primal configuration, we introduce the idea of protection circles that contain segments of key geological boundaries, while in order to generate a dual-cell feature aligned grid, we construct halos around key geological features. The grids generated are employed to study comparative performance of cell-centred versus cell-vertex flux-continuous control-volume distributed multi-point flux approximation (CVD-MPFA) finite-volume formulations using equivalent degrees of freedom and thus ensure application of the most efficient methods. The CVD-MPFA formulation (c.f. Edwards et al.) in cell-centred and cell-vertex modes is somewhat analogous and requires switching control-volume from primal to dual or vice versa, together with appropriate data structures and boundary conditions, however dual-cells are generated after primal grid generation. The relative benefits of both types of approximation, i.e., cell-centred versus vertex-centred, are contrasted in terms of flow resolution and degrees of freedom required