Title :
A finite-element mesh generator based on growing neural networks
Author :
Triantafyllidis, Dimitris G. ; Labridis, Dimitris P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Aristotle Univ. of Thessaloniki, Greece
fDate :
11/1/2002 12:00:00 AM
Abstract :
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.
Keywords :
computational complexity; learning (artificial intelligence); mesh generation; neural nets; probability; Delaunay triangulation; automatic mesh generation; best matching unit location; computational complexity; finite-element method; let-it-grow neural networks; mesh density prediction; probability distribution; training process; Acceleration; Artificial neural networks; Conductors; Finite element methods; Geometry; Helium; Mesh generation; Neural networks; Probability distribution; Production;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.804223