2006
DOI: 10.1090/s0033-569x-06-01036-x
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Electromagnetic inverse problems involving distributions of dielectric mechanisms and parameters

Abstract: Abstract. We consider electromagnetic interrogation problems for complex materials involving distributions of polarization mechanisms and also distributions for the parameters in these mechanisms. A theoretical and computational framework for such problems is given. Computational results for specific problems with multiple Debye mechanisms are given in the case of discrete, uniform, log-normal, and log-bi-Gaussian distributions. Introduction.For at least the past century [52,53], scientific investigators have … Show more

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Cited by 33 publications
(48 citation statements)
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“…This quantification, typically in the form of confidence bounds, is premised upon the accurate specification of the statistical model (14) which links the model to the data. In this report, the model was fit to the data in an ordinary least squares sense, with the tacit assumption that the error random variables E kj have mean zero and constant variance.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…This quantification, typically in the form of confidence bounds, is premised upon the accurate specification of the statistical model (14) which links the model to the data. In this report, the model was fit to the data in an ordinary least squares sense, with the tacit assumption that the error random variables E kj have mean zero and constant variance.…”
Section: Discussionmentioning
confidence: 99%
“…The fragmentation models used with the CFSE data can be considered as what have been termed Aggregate Data/Aggregate Dynamics or Type II inverse problems as presented in [1,Chapter 14] and [5]. Such problems are also common in investigations with models for electromagnetic propagation in inhomogeneous dielectric materials including biotissue [13,14], vibrational dissipation in viscoelastic materials [16], and HIV cellular progression models [1,4,5]. To better understand rates at the generation number cohort or division number cohort level, one should attempt to develop individual (cohort) dynamics to investigate the CFSE data in a Type I framework of Aggregate Data/Individual (Cohort) Dynamics inverse problems such as those discussed in [1,Chapter 14] and [5].…”
Section: Introductionmentioning
confidence: 99%
“…No dynamics are available for individual trajectories x(t, q) for a given q ∈ Q. Such problems arise in viscoelasticity and electromagnetics as well as biology [3,5,6,12,23]. While the approximations we discuss in this paper are applicable to all three types of problems, we shall illustrate the computational results in the context of size-structured marine populations where the inverse problems are of Type II.…”
Section: Introduction and Theoretical Backgroundmentioning
confidence: 99%
“…That is, one must use aggregate (or population level) data in an attempt to describe what are ultimately the dynamics of individual cells. This type of inverse problem is well known in mathematics, and successful mathematical models have been developed and fit to data in a variety of applications such as size-structured marine and insect population models [4,7,11,12], wave propagation models for viscoelastic solids [19], electromagnetic wave propagation [13,14], physiologically-based pharmacokinetics models [6,20], and HIV models [5]. In addition to these applications, theory for such inverse problems is well-developed [3,6,18].…”
Section: Overviewmentioning
confidence: 99%