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arXiv 2006.12757math.APmath.FA
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抛物型PDE中矩阵扩散系数的识别

Identification of matrix diffusion coefficients in a parabolic PDE

Subhankar Mondal, M. Thamban Nair

摘要:

本文考虑了抛物型偏微分方程中矩阵形式扩散系数的反问题。2006年,Cao和Perevezev使用\textit{natural linearisation}方法来识别抛物型PDE中的标量值扩散系数。在本文中,我们利用这一思想来识别一个矩阵值系数,即,使用一个抛物型偏微分方程的弱解的概念,我们将我们的非线性反问题转化为一个问题的解决不适定的算子方程,其中的操作依赖于数据是线性的。为了得到稳定的近似解,采用了Tikhonov正则化方法,并得到了噪声数据下的误差估计。我们还证明了反问题解的唯一性,并给出了所涉及的线性算子的伴随的显式表示。为了在有限维环境下获得误差估计,利用Galerkin方法,通过定义矩阵空间上的正交投影,利用$L^2(Ø)$上的标准正交投影,得到元素来自$L^2(Ø)$的矩阵空间上的正交投影。对于正则化参数的选择,我们采用了自适应技术,使我们有一个顺序最优的收敛速率。最后,对于松弛的噪声数据,我们描述了一个过程,以获得一个平滑的版本,从而获得误差估计。

英文摘要:

We consider an inverse problem of identifying the diffusion coefficient in matrix form in a parabolic PDE. In 2006, Cao and Pereverzev, used a \textit{natural linearisation} method for identifying a scalar valued diffusion coefficient in a parabolic PDE. In this paper, we make use of that idea for identifying a matrix valued coefficient, namely, using the notion of a weak solution for a parabolic PDE, we transform our non-linear inverse problem into a problem of solving an ill-posed operator equation where the operator depending on the data is linear. For the purpose of obtaining stable approximate solutions, Tikhonov regularization is employed, and error estimates under noisy data are derived. We have also showed the uniqueness of the solution of the inverse problem under some assumptions on the data and obtained explicit representation of adjoint of the linear operator involved. For the obtaining error estimates in the finite dimensional setting, Galerkin method is used, by defining orthogonal projections on the space of matrices with entries from $L^2(Ø)$, by making use of standard orthogonal projections on $L^2(Ø)$. For choosing the regularizing parameter, we used the adaptive technique, so that we have an order optimal rate of convergence. Finally, for the relaxed noisy data, we described a procedure for obtaining a smoothed version so as to obtain the error estimates.

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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