A SELF-ORGANIZING NEURAL NETWORK APPROACH FOR AUTOMATIC MESH GENERATION

被引：26
|
作者：
AHN, CH
LEE, SS
LEE, HJ
LEE, SY
机构：
[1] Korea Advanced Institute of Science and Technology, Seoul, 130–650
来源：
IEEE TRANSACTIONS ON MAGNETICS | 1991年 / 27卷 / 05期
关键词：
D O I：
10.1109/20.105028
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A new automatic mesh generator, SOFT (Self-Organizing Finite-element Tessellation), based on self-organizing neural networks has been demonstrated. With user-supplied mesh density function and boundary mesh this mesh generator provides a graded mesh, of which asymptotic characteristics are quite similar to weighted Dirichlet tessellation and dual Delaunay triangulation. Local mesh restrictions such as fixed boundary and/or internal meshes are easily incorporated in this new mesh generator. Although the algorithm is applicable to general n-dimensional meshes 2-dimensional rectangular and triangular meshes are presented for simplicity.
引用
收藏
页码：4201 / 4204
页数：4
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