一种用于识别弱奇异松弛核的线性算法
A linear algorithm for the identification of a weakly singular relaxation kernel using two boundary measurements
AI总结：
本文提出通过两个边界测量来识别弱奇异松弛核，将问题转化为线性反卷积问题，适用于具有记忆特性的扩散过程和粘弹性研究。
摘要：
我们考虑一种在具有记忆的扩散过程和粘弹性研究中遇到的分布式系统。此类系统的关键特征是由于记忆描述的松弛核导致的过去行为对未来的影响。核的参数需要从实验测量中推断出来。本文的主要结果是通过使用两个边界测量，将识别松弛核（通常假设为阿贝尔核的线性组合）的问题转化为求解一个（线性）反卷积问题。
英文摘要：
We consider a distributed system of a type which is encountered in the study of diffusion processes with memory and in viscoelasticity. The key feature of such system is the persistence in the future of the past actions due the memory described via a certain relaxation kernel, see below. The parameters of the kernel have to be inferred from experimental measurements. Our main result in this paper is that by using two boundary measurements the identification of a relaxation kernel which is linear combination of Abel kernels (as often assumed in applications) can be reduced to the solution of a (linear) deconvolution problem.
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。
