• 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