  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.
