• DocumentCode
    1803372
  • Title

    Quantitatively modeling multiple phase transformations in metals using generalized Hopfield nets

  • Author

    Schmitter, Ernst D.

  • Author_Institution
    Univ. of Appl. Sci., Osnabrueck, Germany
  • Volume
    6
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    3904
  • Abstract
    A cube of M=N3 neurons representing cells in a metal is used to model multiple phase transformations. After parameter fitting to experimental data the Hopfield net is able to predict industrially relevant processes quantitatively including their local behaviour
  • Keywords
    Hopfield neural nets; grain boundaries; metals; phase transformations; physics computing; solid-state phase transformations; generalized Hopfield neural nets; metals; multiple phase transformations; quantitative modeling; Aggregates; Entropy; Ferrites; Fitting; Neural networks; Neurons; Predictive models; Solid modeling; Steel; Temperature;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.830779
  • Filename
    830779
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1803372