基于边界控制方法的变密度重构逆问题的深度学习与连续迭代

Klibanov Michael V, Timonov Alexandre
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引用次数: 0

摘要

首次提出并实现了两种方法,即深度学习和连续迭代,用于增强边界控制方法重建的图像。连续迭代的构造是基于将时域波动方程的非线性逆问题简化为第一类线性积分方程。在二维和三维数值实验中证明了数值技术的计算有效性。
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DEEP LEARNING AND SUCCESSIVE ITERATIONS FOR AN INVERSE PROBLEM OF THE VARIABLE DENSITY RECONSTRUCTION BY THE BOUNDARY CONTROL METHOD
For the first time, two approaches, the deep learning and successive iterations, are proposed for use and implemented for enhancing the images reconstructed by the boundary control method. The construction of successive iterations is based on the reduction of a nonlinear inverse problem for a time-domain wave equation to a linear integral equation of the first kind. The computational effectiveness of the numerical techniques is demonstrated in the numerical experiments in two and three dimensions.
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