arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2406.02119math.NAcs.NA
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一种求解抛物型反问题的新型模型降阶方法

A novel model reduction method to solve inverse problems of parabolic type

Wenlong Zhang, Zhiwen Zhang

AI总结:

本文提出基于POD的模型降阶方法解决抛物型反问题中的反演犯罪,涵盖反初值与反源问题,在降低正向模型复杂度的同时保证解精度,并证明了收敛性。

摘要:

本文提出了一种基于本征正交分解(POD)的新型模型降阶方法,有效解决了求解抛物型反问题中的反演犯罪问题。研究了反初值问题和反源问题。利用数据中固有的低维结构,该方法能够在不降低反问题解精度的前提下降低正向模型的复杂度。此外,我们证明了所提方法求解抛物型反问题的收敛性。通过大量实验和对比分析,证明了该方法在克服反演犯罪和获得更优反问题解方面的有效性。所提POD模型降阶方法为提升各领域反问题求解技术的可靠性和适用性提供了有前景的方向。

英文摘要:

In this paper, we propose novel proper orthogonal decomposition (POD)--based model reduction methods that effectively address the issue of inverse crime in solving parabolic inverse problems. Both the inverse initial value problems and inverse source problems are studied. By leveraging the inherent low-dimensional structures present in the data, our approach enables a reduction in the forward model complexity without compromising the accuracy of the inverse problem solution. Besides, we prove the convergence analysis of the proposed methods for solving parabolic inverse problems. Through extensive experimentation and comparative analysis, we demonstrate the effectiveness of our method in overcoming inverse crime and achieving improved inverse problem solutions. The proposed POD model reduction method offers a promising direction for improving the reliability and applicability of inverse problem-solving techniques in various domains.

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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