DocumentCode :
876869
Title :
A self-organizing neural network approach for automatic mesh generation
Author :
Ahn, Chang-Hoi ; Lee, Sang-Soo ; Lee, Hyuek-Jae ; Lee, Soo-Young
Author_Institution :
Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea
Volume :
27
Issue :
5
fYear :
1991
fDate :
9/1/1991 12:00:00 AM
Firstpage :
4201
Lastpage :
4204
Abstract :
An 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, with asymptotic characteristics 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 mesh generator. Although the algorithm is applicable to general n-dimensional meshes, two-dimensional rectangular and triangular meshes are presented for simplicity.
Keywords :
electrical engineering computing; electromagnetic field theory; finite element analysis; neural nets; 2D rectangular meshes; FEA; SOFT; asymptotic characteristics; automatic mesh generation; boundary mesh; electromagnetic fields; finite element analysis; fixed boundary; graded mesh; internal meshes; self-organizing finite-element tessellation; self-organizing neural networks; triangular meshes; user-supplied mesh density function; Biological neural networks; Character generation; Density functional theory; Finite element methods; Mesh generation; Neural networks; Organizing; Solids; Tellurium; Topology;
fLanguage :
English
Journal_Title :
Magnetics, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9464
Type :
jour
DOI :
10.1109/20.105028
Filename :
105028
Link To Document :
بازگشت