• DocumentCode
    948952
  • 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
  • Volume
    13
  • Issue
    6
  • fYear
    2002
  • fDate
    11/1/2002 12:00:00 AM
  • Firstpage
    1482
  • Lastpage
    1496
  • 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;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2002.804223
  • Filename
    1058082