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A finite-element mesh generator based on growing neural networks

机译:基于成长神经网络的有限元网格生成器

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A mesh generator for the production of high-quality finite-element meshes is being proposed. The mesh generator uses an artificial neural network, which grows during the training process in order to adapt itself to a prespecified probability distribution. The initial mesh is a constrained Delaunay triangulation of the domain to be triangulated. Two new algorithms to accelerate the location of the best matching unit are introduced. The mesh generator has been found able to produce meshes of high quality in a number of classic cases examined and is highly suited for problems where the mesh density vector can be calculated in advance.
机译:提出了一种用于产生高质量有限元网格的网格生成器。网格生成器使用人工神经网络,该人工神经网络会在训练过程中增长,以使其自身适应预定的概率分布。初始网格是要三角剖分的区域的约束Delaunay三角剖分。引入了两种新算法来加速最佳匹配单元的定位。已经发现,在检查的许多经典情况下,网格生成器都可以生成高质量的网格,并且非常适合可以提前计算网格密度矢量的问题。

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