Types Of Inverse Problems Research Articles

A modified Richards equation, its adjoint, and a new perspective on land data assimilation

Richards equation, which is the key relationship for addressing basic soil hydrological processes within the rhizosphere and constitutes the basis for multiple-layer soil hydrology models for meteorological studies, is modified to include two vegetation processes: the transpiration (daytime) and hydraulic lift (nighttime). The analytical form of the adjoint system is derived and can serve as the guideline for discrete forms, as implemented in a variety of multiple layer land surface models. The modified equation and its adjoint model may be used to address at least two types of inverse problems: estimation of the initial soil moisture contents from sparse observations of related quantities; and retrieval of critical soil/vegetation parameters with observations of soil moisture contents (or other related quantities) distributed over a certain period of time. The former may be of more interest for numerical weather prediction studies, whereas the latter may be more valuable for longer time integration of climate models.

Inversion of a seismic transmission response

Traveling waves are used not only in exploration geophysics but also in other disciplines faced with remote detection problems. A physical system may be described in terms of the input (the source), the medium, and the output (the received signal). The received signal can be made up of either transmitted waves or reflected waves. Two types of inverse problems can be considered, namely, the inverse source problem and the inverse medium problem. In the inverse source problem, the objective is to determine the source. In the inverse medium problem, the objective is to determine the medium. Thus, in terms of this general classification, four types of problems can be encountered, namely, an inverse source problem with transmitted waves, an inverse source problem with reflected waves, an inverse medium problem with transmitted waves, and an inverse medium problem with reflected waves. Let us look at nature. Twinkle, twinkle, little star. The transmission of starlight though the atmosphere makes the star twinkle. A better image of the star can be obtained by solving an inverse source problem using the transmitted starlight. In the typical inverse source problem, the source of energy is remote, the medium transmits the source signal, and the received data are the transmitted waves. Examples are classical earthquake seismology, radio transmission, and passive sonar. Shakespeare said; “For the eye sees not by itself, but by reflection.” Thus the miracle of eyesight solves an inverse medium problem that uses reflected waves. In the typical inverse medium problem, the source of energy is local and often man‐made, the medium reflects the source signal, and the received data are the reflected waves. Examples are reflection seismology, radar, and active sonar. Thus, the two principle types of inverse problems encountered in nature are the inverse source problem with transmitted waves and the inverse medium problem with reflected waves.

INVERSE FORCED CONVECTION PROBLEM OF SIMULTANEOUS ESTIMATION OF TWO BOUNDARY HEAT FLUXES IN IRREGULARLY SHAPED CHANNELS

This article deals with the use of the conjugate gradient method of function estimation for the simultaneous identification of two unknown boundary heat fluxes in channels with laminar flows. The irregularly shaped channel in the physical domain is transformed into a parallel plate channel in the computational domain by using an elliptic scheme of numerical grid generation. The direct problem, as well as the auxiliary problems and the gradient equations, required for the solution of the inverse problem with the conjugate gradient method are formulated in terms of generalized boundary-fitted coordinates. Therefore, the solution approach presented here can be readily applied to forced convection boundary inverse problems in channels of any shape. Direct and auxiliary problems are solved with finite volumes. The numerical solution for the direct problem is validated by comparing the results obtained here with benchmark solutions for smoothly expanding channels. Simulated temperature measurements containing random errors are used in the inverse analysis for strict cases involving functional forms with discontinuities and sharp corners for the unknown functions. The estimation of three different types of inverse problems are addressed in the paper: (i) time-dependent heat fluxes; (ii) spatially dependent heat fluxes; and (iii) time and spatially dependent heat fluxes.

Inverse convection problem of simultaneous estimation of two boundary heat fluxes in parallel plate channels

This paper deals with the use of the conjugate gradient method of function estimation for the simultaneous identification of two unknown boundary heat fluxes in parallel plate channels. The fluid flow is assumed to be laminar and hydrodynamically developed. Temperature measurements taken inside the channel are used in the inverse analysis. The accuracy of the present solution approach is examined by using simulated measurements containing random errors, for strict cases involving functional forms with discontinuities and sharp-corners for the unknown functions. Three different types of inverse problems are addressed in the paper, involving the estimation of: (i) Spatially dependent heat fluxes; (ii) Time-dependent heat fluxes; and (iii) Time and spatially dependent heat fluxes.

Some uniqueness theorems for mammography-related time-domain inverse problems for the diffusion equation

Some mammography-related inverse problems for the time-dependent diffusion equation are considered. The extrapolated boundary condition is used and the problem is linearized. Uniqueness theorems for two different types of inverse problems are given, namely the determination of the diffusion or absorption coefficients from the reflection data and the simultaneous determination of the diffusion and absorption coefficients from the transmission data. The inverse problem for the latter is reduced to two tomography problems which can be solved successively.

Theory of eddy current inversion

The inverse eddy current problem can be described as the task of reconstructing an unknown distribution of electrical conductivity from eddy-current probe impedance measurements recorded as a function of probe position, excitation frequency, or both. In eddy current nondestructive evaluation, this is widely recognized as a central theoretical problem whose solution is likely to have a significant impact on the characterization of flaws in conducting materials. Because the inverse problem is nonlinear, we propose using an iterative least-squares algorithm for recovering the conductivity. In this algorithm, the conductivity distribution sought minimizes the mean-square difference between the predicted and measured impedance values. The gradient of the impedance plays a fundamental role since it tells us how to update the conductivity in such a way as to guarantee a reduction in the mean-square difference. The impedance gradient is obtained in analytic form using function-space methods. The resulting expression is independent of the type of discretization ultimately chosen to approximate the flaw, and thus has greater generality than an approach in which discretization is performed first. The gradient is derived from the solution to two forward problems: an ordinary and an ‘‘adjoint’’ problem. In contrast, a finite difference computation of the gradient requires the solution of multiple forward problems, one for each unknown parameter used in modeling the flaw. Two general types of inverse problems are considered: the reconstruction of a conductivity distribution, and the reconstruction of the shape of an inclusion or crack whose conductivity is known or assumed to be zero. A layered conductor with unknown layer conductivities is treated as an example of the first type of inversion problem. An ellipsoidal crack is presented as an example of the second type of inversion problem.