DocumentCode :
1947101
Title :
The Neural Network Approach to Automatic Construction of Adaptive Meshes on Multiply-connected Domains
Author :
Nechaeva, Olga
Author_Institution :
Novosibirsk State Univ., Novosibirsk
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
1912
Lastpage :
1917
Abstract :
The neural network approach to automatic construction of adaptive meshes, which we have developed for simply-connected domains, is here extended to the case of multiply-connected domains, i.e. those with holes. This approach is based on Kohonen´s self-organizing maps (SOM) and refers to a class of methods in which an adaptive mesh is a result of transformation of a fixed uniform mesh. Within the approach, a composite algorithm has been proposed in which the SOM algorithm is applied alternatively to boundary and interior mesh nodes. In the case of multiply-connected domains, this algorithm is applied to specify automatically the holes in a fixed mesh. Also, a modified composite algorithm is proposed that provides the consistency of SOM algorithms alternatively applied to both the outer and inner borders and to the interior of the domain. The mesh smoothing algorithm is proposed for multiply-connected domains. The quality of the resulting meshes is admissible according to generally accepted quality criteria.
Keywords :
mesh generation; neural nets; self-organising feature maps; Kohonen´s self-organizing maps; SOM; automatic adaptive mesh construction; mesh smoothing algorithm; multiply-connected domain; neural network approach; Adaptive systems; Boundary conditions; Conformal mapping; Density functional theory; Mathematics; Neural networks; Partial differential equations; Physics computing; Self organizing feature maps; Smoothing methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371250
Filename :
4371250
Link To Document :
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