The Multi-Dimensional Refinement Indicators Algorithm for Optimal Parameterization
Abstract
The estimation of distributed parameters in partial differential equations (PDE) from measures of the solution of the PDE may lead to under-determination problems. The choice of a parameterization is a usual way of adding a-priori information by reducing the number of unknowns according to the physics of the problem. The refinement indicators algorithm provides a fruitful adaptive parameterization technique that parsimoniously opens the degrees of freedom in an iterative way. We present a new general form of the refinement indicators algorithm that is applicable to the estimation of multi-dimensional parameters in any PDE. In the linear case, we state the relationship between the refinement indicator and the decrease of the usual least-squares data misfit objective function. We give numerical results in the simple case of the identity model, and this application reveals the refinement indicators algorithm as an image segmentation technique.
Cite
@article{arxiv.math/0606747,
title = {The Multi-Dimensional Refinement Indicators Algorithm for Optimal Parameterization},
author = {Hend Ben Ameur and François Clément and Pierre Weis and Guy Chavent},
journal= {arXiv preprint arXiv:math/0606747},
year = {2008}
}