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arXiv 0904.2601math.APcs.NAmath.NA
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均匀化与反均匀化中的离散几何结构及其在电阻抗成像中的应用

Discrete Geometric Structures in Homogenization and Inverse Homogenization with application to EIT

California Institute of Technology(加州理工学院)

Mathieu Desbrun, Roger D. Donaldson, Houman Owhadi

AI总结:

针对二维粗糙电导率系数散度型椭圆算子,引入基于凸函数和无散矩阵的离散几何方法,将均匀化转化为线性插值或体积平均,并应用于电阻抗成像。

摘要:

我们引入了一种新的几何方法,用于二维中具有粗糙电导率系数$σ(x)$的散度型椭圆算子的均匀化和反均匀化。我们证明电导率系数与无散矩阵和凸函数$s(x)$在区域$Ω$上是一一对应的。尽管均匀化直接作用于电导率系数时是非线性且非单射的,但当用凸函数重新表达时,均匀化成为$Ω$三角剖分上的线性插值算子,而当用无散矩阵重新表达时,成为体积平均算子。通过使用最优加权Delaunay三角剖分对凸函数进行线性插值,我们获得了一种对任意粗糙系数最优鲁棒的均匀化算法。接下来,我们考虑反均匀化,并展示如何将其分解为一个线性不适定问题和一个适定非线性问题。我们将这种新的几何方法应用于电阻抗成像(EIT)。已知EIT问题至多有一个各向同性解。如果各向同性解存在,我们展示如何从任何具有相同边界Dirichlet-to-Neumann映射的电导率计算它。已知EIT问题在无散矩阵空间中存在唯一(关于$G$-收敛稳定的)解。因此,我们建议凸函数空间是参数化EIT问题解的自然空间。

英文摘要:

We introduce a new geometric approach for the homogenization and inverse homogenization of the divergence form elliptic operator with rough conductivity coefficients $σ(x)$ in dimension two. We show that conductivity coefficients are in one-to-one correspondence with divergence-free matrices and convex functions $s(x)$ over the domain $Ω$. Although homogenization is a non-linear and non-injective operator when applied directly to conductivity coefficients, homogenization becomes a linear interpolation operator over triangulations of $Ω$ when re-expressed using convex functions, and is a volume averaging operator when re-expressed with divergence-free matrices. Using optimal weighted Delaunay triangulations for linearly interpolating convex functions, we obtain an optimally robust homogenization algorithm for arbitrary rough coefficients. Next, we consider inverse homogenization and show how to decompose it into a linear ill-posed problem and a well-posed non-linear problem. We apply this new geometric approach to Electrical Impedance Tomography (EIT). It is known that the EIT problem admits at most one isotropic solution. If an isotropic solution exists, we show how to compute it from any conductivity having the same boundary Dirichlet-to-Neumann map. It is known that the EIT problem admits a unique (stable with respect to $G$-convergence) solution in the space of divergence-free matrices. As such we suggest that the space of convex functions is the natural space in which to parameterize solutions of the EIT problem.

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