Adaptive sampling and mesh generation

Abstract

A principle in surface sampling and mesh generation is that highly curved areas should be sampled densely and vice versa. The paper presents an approach for automated surface sampling and adaptive mesh generation in accordance with this principle. The approach is self-organizing, forming topology-preserving mesh from random initialization. Mesh spacing versus surface curvedness can be easily controlled by a single parameter in the shape function. Key locations can be prescribed by imposing additional boundary conditions. Experiments are presented.