arXivDaily arXiv每日学术速递 周一至周五更新
arXiv math/0606747math.NAcs.NA

多维细化指标算法用于最优参数化

The Multi-Dimensional Refinement Indicators Algorithm for Optimal Parameterization

Hend Ben Ameur, François Clément, Pierre Weis, Guy Chavent

AI总结:

提出一种适用于任意偏微分方程中多维参数估计的通用细化指标算法,在线性情况下建立了细化指标与最小二乘数据拟合目标函数下降之间的关系,并在恒等模型下展示了其作为图像分割技术的应用。

摘要:

从偏微分方程解的测量中估计分布参数可能导致欠定问题。参数化的选择是一种通过根据问题的物理性质减少未知数数量来添加先验信息的常用方法。细化指标算法提供了一种有效的自适应参数化技术,以迭代方式稀疏地打开自由度。我们提出了一种新的细化指标算法的一般形式,适用于任意偏微分方程中多维参数的估计。在线性情况下,我们陈述了细化指标与通常的最小二乘数据拟合目标函数下降之间的关系。我们在恒等模型的简单情况下给出了数值结果,该应用揭示了细化指标算法作为一种图像分割技术。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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