This may lead to the misconception that point cloud generation should be as tough as mesh generation. Often they test proposed meshfree algorithms on point clouds generated as nodes of meshes which are obtained by a standard mesh generation method. Most published research articles on meshfree methods take this for granted and do not discuss how the point cloud generation should be done. There remains, however, a more subtle question whether good meshfree point clouds are much easier to generate than good meshes. The initial step of mesh generation is replaced by the generation of a meshfree point cloud that does not need to be topologically connected, unlike meshes, which is an easier task per se. In such cases, mesh adaptation or regeneration has to be done automatically and may easily become a computational bottleneck.Īs a result, over the past two decades, meshfree methods have become a popular alternative to mesh-based simulations. They have been widely used especially for applications where the computational domain can undergo rapid or huge changes in time, such as large deformations and displacements. Meshfree methods arose in the first instance in order to prevent this need of mesh generation. Moreover, high quality mesh generation cannot always be entirely automated, and often requires a lot of manual work for complicated domains. The efficiency of mesh generation limits the overall accuracy, robustness and speed of the numerical simulation process. Despite advances in mesh generation technology and computer hardware, the generation and management of meshes is often the most difficult and time consuming part of the simulation procedure on geometrically complex domains. Most numerical methods for solving partial differential equations require the generation of a mesh over the computational domain.
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