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arXiv 2403.11599math.APcs.NAmath.NA
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一维次扩散模型中不相交时间集数据下的逆系数问题

Inverse Coefficient Problem for One-Dimensional Subdiffusion with Data on Disjoint Sets in Time

Siyu Cen, Bangti Jin, Yavar Kian, Eric Soccorsi, Rachid Zarouf, Zhi Zhou

AI总结:

本文针对一维次扩散模型,利用不相交时间集上的侧向Cauchy数据,提出逆系数问题并证明唯一性,结合分数阶扩散记忆效应与逆Sturm-Liouville理论,辅以数值实验验证。

摘要:

本文研究一维次扩散模型的逆系数问题,该模型包含时间上的Caputo分数阶导数。逆问题旨在从一对侧向Cauchy数据中确定两个系数及多个参数(阶数和区间长度)。侧向Cauchy数据在时间上不相交的集合上给出,且仅有一次激励,测量在激励支撑之外的时间序列上进行。我们针对不同的侧向Cauchy数据证明了两个唯一性结果。分析基于解的表示、观测的解析性以及Sini [35]提出的改进版逆Sturm-Liouville理论。我们的结果充分利用了分数阶扩散的记忆效应来实现模型中系数的唯一恢复。此外,还进行了若干数值实验以补充分析。

英文摘要:

In this work we investigate an inverse coefficient problem for the one-dimensional subdiffusion model, which involves a Caputo fractional derivative in time. The inverse problem is to determine two coefficients and multiple parameters (the order, and length of the interval) from one pair of lateral Cauchy data. The lateral Cauchy data are given on disjoint sets in time with a single excitation and the measurement is made on a time sequence located outside the support of the excitation. We prove two uniqueness results for different lateral Cauchy data. The analysis is based on the solution representation, analyticity of the observation and a refined version of inverse Sturm-Liouville theory due to Sini [35]. Our results heavily exploit the memory effect of fractional diffusion for the unique recovery of the coefficients in the model. Several numerical experiments are also presented to complement the analysis.

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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