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.
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Alexander, S.K., Fieguth, P., Vrscay, E.R. (2004). Parameterized Hierarchical Annealing for Scientific Models. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_30
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DOI: https://doi.org/10.1007/978-3-540-30125-7_30
Publisher Name: Springer, Berlin, Heidelberg
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