|Title||Hierarchical annealing for scientific models|
|Publication Type||Conference Paper|
|Year of Publication||2004|
|Authors||Alexander, S. K., P. Fieguth, and E. Vrscay|
|Conference Name||37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)|
|Keywords||2D domains, 3D domains, complex model sampling, computational complexity, data visualisation, hierarchical annealing, hierarchical sampling, image resolution, image sampling, pixellated lattice, porous materials, porous media, sampled images, scientific models, simulated annealing|
The computational complexity of simulated annealing makes it an impractical tool in many applications, particularly for complex, non-local models on very large 2D and 3D domains as desired in many scientific contexts. In particular, it is very difficult to produce large scale structure from a fine, pixellated lattice. Thus a hierarchical approach is intuitively attractive. However, existing approaches are few and limited. Motivated by a current problem in porous media, we develop a hierarchical approach to complex model sampling. In experiments, this approach results in 1-2 orders of magnitude computational gain, and significant gains in convergence as well.
Hierarchical annealing for scientific models