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Parameterized Hierarchical Annealing for Scientific Models

  • Simon K. Alexander
  • Paul Fieguth
  • Edward R. Vrscay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

The accurate synthesis of binary porous media is a difficult problem. Initial applications of simulated annealing in this context with small data sets and simple energy functions have met with limited success. Simulated annealing has been applied to a wide variety of problems in image processing. Particularly in scientific applications such as discussed here, the computational complexity of this approach may constrain its effectiveness; complex, non-local models on large 2D and 3D domains may be desired, but do not lend themselves to traditional simulated annealing due to computational cost. These considerations naturally lead to a wish for hierarchical/multiscale methods. However, existing methods are few and limited. In this paper a method of hierarchical simulated annealing is discussed, and a simple parameterization proposed to address the problem of moving through the hierarchy. This approach shows significant gains in convergence and computational complexity when compared to the simulated annealing algorithm.

Keywords

Porous Medium Simulated Annealing Simulated Annealing Algorithm Coarse Scale Hierarchical Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Simon K. Alexander
    • 1
  • Paul Fieguth
    • 2
  • Edward R. Vrscay
    • 1
  1. 1.Department of Applied MathematicsUniversity of WaterlooWaterlooCanada
  2. 2.Department of Systems Design EngineeringUniversity of WaterlooWaterlooCanada

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