Meshless methods are an active and modern branch of numerical analysis with many intriguing benefits. One of the main open research questions related to local meshless methods is how to select the best possible stencil - a collection of neighbouring nodes - to base the calculation on. In this paper, we describe the procedure for generating a labelled stencil dataset and use a variation of pointNet - a deep learning network based on point clouds - to create a classifier for the quality of the stencil. We exploit features of pointNet to implement a model that can be used to classify differently sized stencils and compare it against models dedicated to a single stencil size. The model is particularly good at detecting the best and the worst stencils with a respectable area under the curve (AUC) metric of around 0.90. There is much potential for further improvement and direct application in the meshless domain.

翻译：无网点方法是一个活跃的现代数字分析分支,有许多令人感兴趣的好处。与本地无网点方法有关的主要开放研究问题之一是如何选择最可能的线性线――一个相邻节点的集合――作为计算基础。在本文中,我们描述了制作标签的线性线数据集的程序,并使用点网的变异(基于点云的深层学习网络)来为线性线质量建立一个分类器。我们利用点网的特征来实施一种模型,该模型可用于对不同尺寸的线性线性线进行分类,并与用于单一线性线性线性的模型进行比较。该模型在探测最佳和最坏的线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线下0.90左右的曲线度线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线能和直接应用。我们有很大潜力进一步改进和无孔性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线性线。