Convexification numerical algorithm for a 2D inverse scattering problem with backscatter data

When we use a discretization by finite differences, to solve differential equations we find problems at the border of the domain of the solution. If the solution is also immersed in a ill-posed inverse problem; we can find very bad solutions. In this paper we apply a discretization of two - sided difference quotients method to solve Ill-posed inverse self-adjoint elliptic problem [Kirsch(2011)]. Some numerical examples showing the effectiveness of this method and we will use mollification techniques to smooth the solutions.

Distribution of electrical potentials over the surface of the heart, which is called the epicardial potential distribution, is a valuable tool to understand whether there is a defect in the heart. Direct measurement of these potentials requires highly invasive procedures. An alternative is to reconstruct these epicardial potentials non-invasively from the body surface potentials, which constitutes one form of the ill-posed inverse problem of electrocardiography (ECG). The goal of this study is to solve the inverse problem of ECG using several regularization methods and compare their performances. We employed Tikhonov Regularization, Truncated Singular Value Decomposition (TSVD), Least Squares QR (LSQR) methods in this study. We compared the effectiveness of these regularization methods to solve the ill-posed inverse ECG problem. Some of the regularization methods require a regularization parameter to solve the inverse problem. We used the well-known L-Curve method to obtain the regularization parameter. The performance of the regularization methods for solving the inverse ECG problem was also evaluated based on a realistic heart-torso model simulation protocol. In this paper, we also investigated the usage of genetic algorithm (GA) for regularizing the ill-posed inverse ECG problem. The results showed that GA can be applied to regularize the ill-posed problem when combined with the results of conventional regularization methods or additional information about solutions.

Accelerometers have been widely used in almost every area of science and engineering. They are supposed to be physical instruments for measuring the acceleration of a moving object. Although huge technological advance is made in hardware of accelerometers over more than one century, accelerometers have been persistently designed and fabricated mechanically under the framework of forward problems of damped mass–spring systems. We show that accelerometers are essentially inverse ill-posed source problems from the mathematical point of view, implying that small measurement errors of equivalent displacements are inherently amplified significantly such that accelerations output from accelerometers can become extremely noisy, numerically incorrect and physically meaningless. The ill-posedness of accelerometers has been always implicitly circumvented approximately for more than one century. As a result, accelerometers theoretically can only produce approximate outputs of acceleration. Here we present the concept of computerized accelerometers, because inverse ill-posed problems, as in the case of accelerometers, cannot be rigorously solved mechanically. The acceleration can only rigorously be reconstructed computationally as a regularized solution to the inverse ill-posed source problem of acceleration from equivalent displacements. The concept of computerized accelerometers theoretically warrants precise measurement of acceleration without approximation, is valid for nonlinear time-dependent damping as well and provides a turning point for accelerometers to become a fully rigorous computerized physical instrument. Simulated examples have confirmed that least squares reconstruction of acceleration can be too extremely noisy to be physically meaningful and shown that elastic acceleration is incorrect by more than 100%.

The article discusses the problem of determining the learning outcomes for educational programs of the university, including at the program level and the level of individual disciplines included in the program. The goal is to substantiate the understanding of the definition of learning outcomes at a university as an inverse ill-posed problem within the framework of the theory of ill-posed problems, which will allow further reengineering of the educational process at a university based on models of inverse and ill-posed problems. The article identifies the types of uncertainty inherent in education, which affect the possibility of an incorrect decision. The publication substantiates a new understanding of the educational process focused on learning outcomes at a university, as a process of finding a solution to an inverse incorrect problem. In this case, the learning outcomes formulated for a specific educational program are considered as an available solution to the inverse problem. The research carried out by the authors made it possible to construct a mathematical model of the educational process at the university and identify its components. The authors demonstrate that the optimal way to design the educational process is to design it as an inverse problem, and the list of disciplines, their content and the corresponding learning outcomes are determined and developed at subsequent stages, focusing on the goals of the program.

Remote sensing of ocean color from space aims at retrieving from a noisy top-of-atmosphere radiance the values taken by some relevant quantities like the chlorophyll-a concentration or the marine reflectance. From a mathematical perspective, it is an ill-posed inverse problem with a highly nonlinear operator. Few techniques are available in the case of a nonlinear inverse problem; even its theoretical study is far from easy, yet some techniques may be used in a practical setting when the noise distribution is known. However in the case of ocean color remote sensing, the noise encompasses several types of error owing to the forward operator approximation (radiative transfer model) as well as to calibration and pure measurement noise. Hence the noise distribution is unknown. In this work, a stochastic technique is proposed to first infer a noise distribution, which is next used to retrieve the marine reflectance in a least-square prediction setting by a regression model. The methodology is illustrated on actual data originating from the SeaWiFS sensor.

The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solved on nonuniform meshes, arising from a finite element method typical for nonlinear inverse problems, interpolation and embeddings are needed. To overcome this, we present a flexible framework to extend model-based learning directly to nonuniform meshes, by interpreting the mesh as a graph and formulating our network architectures using graph convolutional neural networks. This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh. We present results for Electrical Impedance Tomography, a severely ill-posed nonlinear inverse problem that is frequently solved via optimization-based methods, where the forward problem is solved by finite element methods. Results for absolute EIT imaging are compared to standard iterative methods as well as a graph residual network. We show that the GCNM has good generalizability to different domain shapes and meshes, out of distribution data as well as experimental data, from purely simulated training data and without transfer training.

Electrical Resistance Tomography (ERT) aims to estimate the electrical properties at the interior of an object from current-voltage measurements on its boundary. Image reconstruction of ERT is a nonlinear and ill-posed inverse problem. Regularization technique is used to solve the inverse problems and two-step iterative shrinkage/thresholding algorithm (TwIST) is a relatively effective method. In order to further improve the noise immunity of iterative threshold algorithm, an improved two-step iterative shrinkage/thresholding algorithm based on 3D modelling is presented in this paper. We transform the sensitivity matrix by singular value decomposition (SVD), and then remove the small singular values which are easy to cause the instability of the solution. Both simulation and experimental results demonstrate that the proposed method can improve the imaging quality obviously.

Magnetic induction tomography (MIT) can be used to probe electrical conductivity variation of an object. It offers a nondestructive and contactless means of imaging the internal conductivity distribution of the object. The forward problem in MIT is a general eddy current problem, which is required to estimate the measured data for a given conductivity distribution. The edge finite-element method with a formulation using a magnetic vector potential has been implemented to solve the forward problem. Conductivity reconstruction in MIT is a nonlinear and ill-posed inverse problem. The regularization techniques are required to incorporate a priori knowledge of the conductivity distribution for a stable solution of this inverse problem. This article presents a conductivity interface reconstruction technique, which has an inherent regularization property. Instead of calculating the conductivity distribution in the whole region of interest, the interface between two different conductivities is reconstructed. A narrowband level-set method has been implemented to describe the interfaces. An iterative optimization scheme has been used to modify the interface estimation in each iteration step, so that the predicated forward solution is closed to the measured data. The results are presented using experimental data representative of an application of MIT in molten metal flow visualization.

Geophysical Inverse Theory and Regularization Problems

The scattering of incident plane waves by a penetrable obstacle covered with a thin sheet leads to the inverse problem under consideration. We investigate the recovery of obstacles from measurements of the far-field patterns of scattered fields. The domain derivative of the operator mapping a boundary of an obstacle on the far-field pattern is presented. This result can be used to derive iterative regularization methods solving the ill-posed and nonlinear inverse problem. A suggestion of such a scheme, the Landweber iteration, is discussed in detail and some numerical results show the performance of the algorithm.

In this work, we unveil an analogy between the well-known lattice based learning with error problem and ill-posed inverse problems. We show that LWE problem is a structured inverse problem. Further, we propose a symmetric encryption scheme based on ill-posed problems and thoroughly discuss its security. Finally, we propose a public key encryption scheme based on our symmetric encryption scheme and CRYSTALS-Kyber KEM (key encapsulation mechanism) and discuss its security.

This reference is for an abstract only. A full paper was not submitted for this conference. Abstract AVO (amplitude-varying-with-offset) is a seismic prestack inversion technique for estimating elastic parameters of the subsurface. The seismic AVO inversion problem can be formulated as a linearized or nonlinear inverse problem. It is a multidimensional and highly ill-posed inverse problem. It is affected by strong noise and measurement uncertainty. Therefore, in seismic AVO analysis, the goal is not only to find the model that best-fits the data, but also to characterize the uncertainty of the analysis. Uncertainty characterization of seismic analysis makes the geophysical interpretation more reliable. The Bayesian formalism generally constitutes a powerful approach for solving many seismic inverse problems. It allows combining available prior knowledge with the information contained in the seismic data. The solution of a Bayesian inverse problem is given by the joint posterior distribution of model parameters. Stochastic approximations of the true posterior are exemplified by the MCMC method and more recently by the particle filtering (PF) method. For most seismic inverse problems, stochastic approximation techniques are computationally unappealing. The maximum a posteriori (MAP) is the simplest Bayesian method for estimating model parameters. It provides the explanation that maximizes the posterior distribution. The maximum likelihood (ML) is a popular non-Bayesian method for estimating model parameters. It provides the explanation that maximizes the likelihood of the data. It assumes that the model parameters are deterministic and omits the pertinent prior knowledge on them. In practice, both MAP and ML point estimators have severe problems with over-fitting the data and model order estimation. In this paper, the recently-developed variational Bayesian learning is proposed as a new method for solving the linearized seismic AVO inversion problem. Variational Bayesian learning allows the true joint posterior distribution of model parameters to be approximated by a simpler approximating ensemble for which the required inferences are tractable. The main advantage in resorting to variational Bayesian learning is its robustness to the over-fitting problem, which is major in practice. This probabilistic machine learning method allows finding an optimum balance between the representation power and the model complexity of the data.

Many works have shown strong connections between learning and regularization techniques for ill-posed inverse problems. A careful analysis shows that a rigorous connection between learning and regularization for inverse problem is not straightforward. In this study, pattern recognition will be viewed as an ill-posed inverse problem and applications of methods from the theory of inverse problems to pattern recognition are studied. A new learning algorithm derived from a well-known regularization model is generated and applied to the task of reconstruction of an inhomogeneous object as pattern recognition. Particularly, it is demonstrated that pattern recognition can be reformulated in terms of inverse problems defined by a Riesz-type kernel. This reformulation can be employed to design a learning algorithm based on a numerical solution of a system of linear equations. Finally, numerical experiments have been carried out with synthetic experimental data considering a reasonable level of noise. Good recoveries have been achieved with this methodology, and the results of these simulations are compatible with the existing methods. The comparison results show that the Regularization-based learning algorithm (RBA) obtains a promising performance on the majority of the test problems. In prospects, this method can be used for the creation of automated systems for diagnostics, testing, and control in various fields of scientific and applied research, as well as in industry.

The Cauchy problem associated with the Helmholtz equation is an ill-posed inverse problem that is challenging to solve due to its instability and sensitivity to noise. In this paper, we propose a metaheuristic approach to solve this problem using Genetic Algorithms in conjunction with Tikhonov regularization. Our approach is able to produce stable, convergent, and accurate solutions for the Cauchy problem, even in the presence of noise. Numerical results on both regular and irregular domains show the effectiveness and accuracy of our approach.

Medical image reconstruction is typically an ill-posed inverse problem. In order to address such ill-posed problems, the prior distribution of the sought after object property is usually incorporated by means of some sparsity-promoting regularization. Recently, prior distributions for images estimated using generative adversarial networks (GANs) have shown great promise in regularizing some of these image reconstruction problems. In this work, we apply an image-adaptive GAN-based reconstruction method (IAGAN) to reconstruct high fidelity images from incomplete medical imaging data. It is observed that the IAGAN method can potentially recover fine structures in the object that are relevant for medical diagnosis but may be oversmoothed in reconstructions with traditional sparsity-promoting regularization.