在本文中， 我們將主要討論一種在求解一類波動方程反問題中很有價值的數值方法：線性化方法。 / 在介紹上述的數值方法之前， 我們將首先討論波動方程的一些重要的特質，主要包括四類典型的波動方程模型，方程的基本解和一般解，以及波動方程解的性質。 / 接下來，在本文的第二部分中，我們會首先介紹所求解的模型以及其反問題。此反問題主要研究求解波動方程[附圖]中的系數c. 線性化方法的主要思想在於將速度c分解成兩部分：c₁ 和c₂ ，並且滿足關系式：[附圖]，其中c₁ 是一個小的擾動量。另一方面，上述波動方程的解u 可以被線性表示：u = u₀ + u₁ ，其中u₀ 和u₁ 分別是一維問題和二維問題的解。相應的，我們將運用有限差分方法和傅利葉變換方法求解上述一維問題和二維問題，從而分別求解c₁ 和c₂ ，最終求解得到係數c. 在本文的最後，我們將進行一些數值試驗，從而驗證此線性化方法的有效性和可靠性。 / In this thesis, we will discuss a numerical method of enormous value, a linearized method for solving a certain kind of inverse wave equations. / Before the introduction of the above-mentioned method, we shall discuss some important features of the wave equations in the first part of the thesis, consisting of four typical mathematical models of wave equations, there fundamental solutions, general solutions and the properties of those general solutions. / Next, we shall present the model and its inverse problem of recovering the coefficient c representing the propagation velocity of wave from the wave equation [with mathematic formula] The linearized method aims at dividing the velocity c into two parts, c₀ and c₁, which satisfying the relation [with mathematic formula], where c₁ is a tiny perturbation. On the other hand, the solution u can be represented in the linear form, u = u₀ + u₁, where u₀ and u₁ are the solutions to one-dimensional problem and two- dimensional problem respectively. Accordingly, we can use the numerical methods, finite difference method and Fourier transform method to solve the one-dimensional forward problem and two-dimensional inverse problem respectively, thus we can get c₀ and c₁, a step before we recover the velocity c. In the numerical experiments, we shall test the proposed linearized numerical method for some special examples and demonstrate the effectiveness and robustness of the method. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Xu, Xinyi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 64-65). / Abstracts also in Chinese. / Chapter 1 --- Fundamental aspects of wave equations --- p.6 / Chapter 1.1 --- Introduction --- p.6 / Chapter 1.1.1 --- Four important wave equations --- p.6 / Chapter 1.1.2 --- General form of wave equations --- p.10 / Chapter 1.2 --- Fundamental solutions --- p.11 / Chapter 1.2.1 --- Fourier transform --- p.11 / Chapter 1.2.2 --- Fundamental solution in three-dimensional space --- p.14 / Chapter 1.2.3 --- Fundamental solution in two-dimensional space --- p.16 / Chapter 1.3 --- General solution --- p.19 / Chapter 1.3.1 --- One-dimensional wave equations --- p.19 / Chapter 1.3.2 --- Two and three dimensional wave equations --- p.26 / Chapter 1.3.3 --- n dimensional case --- p.28 / Chapter 1.4 --- Properties of solutions to wave equation --- p.31 / Chapter 1.4.1 --- Properties of Kirchhoff’s solutions --- p.31 / Chapter 1.4.2 --- Properties of Poisson’s solutions --- p.33 / Chapter 1.4.3 --- Decay of the solutions to wave equation --- p.34 / Chapter 2 --- Linearized method for wave equations --- p.36 / Chapter 2.1 --- Introduction --- p.36 / Chapter 2.1.1 --- Background --- p.36 / Chapter 2.1.2 --- Forward and inverse problem --- p.38 / Chapter 2.2 --- Basic ideas of Linearized Method --- p.39 / Chapter 2.3 --- Theoretical analysis on linearized method --- p.41 / Chapter 2.3.1 --- One-dimensional forward problem --- p.42 / Chapter 2.3.2 --- Two-dimensional forward problem --- p.43 / Chapter 2.3.3 --- Existence and uniqueness of solutions to the inverse problem --- p.45 / Chapter 2.4 --- Numerical analysis on linearized method --- p.45 / Chapter 2.4.1 --- Discrete analog of the inverse problem --- p.46 / Chapter 2.4.2 --- Fourier transform --- p.48 / Chapter 2.4.3 --- Direct methods for inverse and forward problems --- p.52 / Chapter 2.5 --- Numerical Simulation --- p.54 / Chapter 2.5.1 --- Special Case --- p.54 / Chapter 2.5.2 --- General Case --- p.59 / Chapter 3 --- Conclusion --- p.63 / Bibliography --- p.64

Inverse problems are mathematically and numerically very challenging due to their inherent ill-posedness in the sense that a small perturbation of the data may cause an enormous deviation of the solution. Regularization methods have been established as the standard approach for their stable numerical solution thanks to the ground-breaking work of late Russian mathematician A.N. Tikhonov. However, existing studies mainly focus on general-purpose regularization procedures rather than exploiting mathematical structures of specific problems for designing efficient numerical procedures. Moreover, the stochastic nature of data noise and model uncertainties is largely ignored, and its effect on the inverse solution is not assessed. This thesis attempts to design some problem-specific efficient numerical methods for the Robin inverse problem and to quantify the associated uncertainties. It consists of two parts: Part I discusses deterministic methods for the Robin inverse problem, while Part II studies stochastic numerics for uncertainty quantification of inverse problems and its implication on the choice of the regularization parameter in Tikhonov regularization. / Key Words: Robin inverse problem, variational approach, preconditioning, Modica-Motorla functional, spectral stochastic approach, Bayesian inference approach, augmented Tikhonov regularization method, regularization parameter, uncertainty quantification, reduced-order modeling / Part I considers the variational approach for reconstructing smooth and nonsmooth coefficients by minimizing a certain functional and its discretization by the finite element method. We propose the L2-norm regularization and the Modica-Mortola functional from phase transition for smooth and nonsmooth coefficients, respectively. The mathematical properties of the formulations and their discrete analogues, e.g. existence of a minimizer, stability (compactness), convexity and differentiability, are studied in detail. The convergence of the finite element approximation is also established. The nonlinear conjugate gradient method and the concave-convex procedure are suggested for solving discrete optimization problems. An efficient preconditioner based on the Sobolev inner product is proposed for justifying the gradient descent and for accelerating its convergence. / Part II studies two promising methodologies, i.e. the spectral stochastic approach (SSA) and the Bayesian inference approach, for uncertainty quantification of inverse problems. The SSA extends the variational approach to the stochastic context by generalized polynomial chaos expansion, and addresses inverse problems under uncertainties, e.g. random data noise and stochastic material properties. The well-posedness of the stochastic variational formulation is studied, and the convergence of its stochastic finite element approximation is established. Bayesian inference provides a natural framework for uncertainty quantification of a specific solution by considering an ensemble of inverse solutions consistent with the given data. To reduce its computational cost for nonlinear inverse problems incurred by repeated evaluation of the forward model, we propose two accelerating techniques by constructing accurate and inexpensive surrogate models, i.e. the proper orthogonal decomposition from reduced-order modeling and the stochastic collocation method from uncertainty propagation. By observing its connection with Tikhonov regularization, we propose two functionals of Tikhonov type that could automatically determine the regularization parameter and accurately detect the noise level. We establish the existence of a minimizer, and the convergence of an alternating iterative algorithm. This opens an avenue for designing fully data-driven inverse techniques. / This thesis considers deterministic and stochastic numerics for inverse problems associated with elliptic partial differential equations. The specific inverse problem under consideration is the Robin inverse problem: estimating the Robin coefficient of a Robin boundary condition from boundary measurements. It arises in diverse industrial applications, e.g. thermal engineering and nondestructive evaluation, where the coefficient profiles material properties on the boundary. / Jin, Bangti. / Adviser: Zou Jun. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3541. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 174-187). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

In this thesis, we will address two most important topics in inverse acoustic and electromagnetic obstacle scattering problems: uniqueness and reconstruction algorithms. / The first part is devoted to the uniqueness issues. A detailed exposition of the background of this problem and a comprehensive discussion of the existing results are presented. The focus of this part is on our contribution to this field, especially on the unique determination of polygonal or polyhedral scatterers with a single or finitely many far-field measurements. In summary, we have shown the following results when the polyhedral type scatterers are concerned in inverse acoustic obstacle scattering: if the scatterer consists of finitely many solid polyhedral obstacles, which may be either sound-soft, sound-hard or two types mixed together, and it may also contain some crack-type obstacles but only sound-soft ones, then one can uniquely determine the scatterer by a single incident plane wave at some fixed k0 > 0 and d0 ∈ SN-1 . This statement is affirmatively verified in any dimensions whenever there is no any sound-hard obstacle present; when there is any sound-hard obstacle, the uniqueness is validated in the R2 case, but still incomplete in the RN case with N ≥ 3, which is proved to be true only by N different incident plane waves. Whenever the scatterer contains some sound-hard crack-type obstacles, we have constructed some examples to show that one cannot uniquely determine the scatterer by any less than N incident waves. So in the case with the additional presence of sound-hard crack-type obstacles, another result we have established that one can uniquely determine such a scatterer by N incident waves at any fixed wave number and arbitrary N linearly independent incident directions is optimal. We also consider more general polyhedral type scatterers with partially coated components, and some uniqueness results are established to determine the underlying physical properties. Besides, we have also collected a global uniqueness result for balls or discs. It is shown that in the resonance region, the shape of a sound-soft/sound-hard ball in R3 or a soundhard/sound-soft disc in R2 is uniquely determined by a single far-field datum measured at some fixed spot corresponding to a single incident plane wave. This seems to be an important result in the uniqueness study field as it is the first to establish the unique determination by a single far-field datum measured at one fixed spot. While all the other existing uniqueness results require far-field data observed at least in one open subset on the unit sphere with non-zero measure. To pave the way for the uniqueness study with such simple balls or discs, we also present a systematic and rather complete study of the interlacing character of the zeros for Bessel and spherical Bessel functions and their respective derivatives. Finally, all the uniqueness results for inverse acoustic obstacle scattering associated with general polyhedral scatterers have been extended to the inverse electromagnetic scattering. / The second part of this thesis is concerned with the reconstruction algorithms. We will present a novel multilevel linear sampling method (MLSM) which is developed in our recent work. The new method resembles the popular multi-level techniques in scientific computing and is shown to possess the asymptotically optimal computational complexity. For an n x n sampling mesh-grid in R2 or an n x n x n sampling mesh-grid in R3 , the proposed algorithm only requires to solve O (nN-1)( N = 2,3) far-field equations for a RN problem, and this is in sharp contrast to the original version of the linear sampling method which needs to solve n N far-field equations instead. Numerical experiments have illustrated the promising feature of the new algorithm in significantly reducing the computational costs. / Liu, Hongyu. / "June 2007." / Adviser: Jun Zou. / Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0354. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 161-168). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Tsang, Ka Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves [121]-123). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.5 / Chapter 2 --- Inverse problems and formulations --- p.6 / Chapter 3 --- Review of some existing methods --- p.8 / Chapter 4 --- Trust Region Method --- p.16 / Chapter 4.1 --- Some Auxiliary Tools --- p.18 / Chapter 4.2 --- Trust Region Algorithm --- p.23 / Chapter 4.3 --- Convergence of trust region method --- p.28 / Chapter 4.3.1 --- Notations and Assumptions --- p.28 / Chapter 4.3.2 --- Convergence for exact data --- p.29 / Chapter 4.3.3 --- Regularity For Inexact Data --- p.36 / Chapter 4.4 --- Experiment On Trust Region Method --- p.39 / Chapter 4.4.1 --- Problem Setting --- p.39 / Chapter 4.4.2 --- Algorithm --- p.40 / Chapter 4.4.3 --- Experiment Results --- p.42 / Chapter 4.5 --- Trust Region Conjugate Gradient Method --- p.46 / Chapter 4.5.1 --- Notations and Assumptions --- p.49 / Chapter 4.5.2 --- Convergence Properties for Exact Data --- p.52 / Chapter 4.5.3 --- Regularity for Inexact Data --- p.57 / Chapter 5 --- Parameter Identification Problems --- p.60 / Chapter 5.1 --- Introduction --- p.60 / Chapter 5.1.1 --- Computation of VJ(x) --- p.67 / Chapter 5.2 --- Algorithm for Parameter Identification Problems --- p.72 / Chapter 5.2.1 --- "Finite Element Method in Two Dimensions:Ω =[0,1] x [0,1]" --- p.75 / Chapter 5.3 --- Experiments on Trust Region-CG Method for Parameter Identification Problems --- p.82 / Chapter 5.3.1 --- One Dimension Problem --- p.82 / Chapter 5.3.2 --- Two Dimensions Problem --- p.95 / Chapter 5.4 --- Conclusion --- p.119 / Bibliography --- p.121

Liu, Keji. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 97-98). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Background and models of inverse scattering --- p.1 / Chapter 1.1 --- Model I --- p.1 / Chapter 1.2 --- Model II --- p.3 / Chapter 1.3 --- Model III --- p.3 / Chapter 2 --- Direct and Inverse Problems of Three Models --- p.6 / Chapter 2.1 --- First Model: Direct Problem --- p.6 / Chapter 2.2 --- First Model: Inverse Problem --- p.10 / Chapter 2.2.1 --- Linear Sampling Method --- p.10 / Chapter 2.2.2 --- Strengthened Linear Sampling Method --- p.12 / Chapter 2.2.3 --- Multilevel Linear Sampling Method --- p.15 / Chapter 2.3 --- Second Model: Direct Problem --- p.19 / Chapter 2.4 --- Second Model: Inverse Problem --- p.21 / Chapter 2.4.1 --- Contrast Source Inversion Method --- p.21 / Chapter 2.4.2 --- Subspace-based Optimization Method --- p.25 / Chapter 2.4.3 --- Multiple Signal Classification Method --- p.31 / Chapter 2.5 --- Third Model: Direct Problem --- p.33 / Chapter 2.6 --- Third Model: Inverse Problem --- p.41 / Chapter 2.6.1 --- Generalized Dual Space Indicator Method --- p.41 / Chapter 3 --- Numerical Simulations --- p.44 / Chapter 3.1 --- Numerical Simulations of First Model --- p.44 / Chapter 3.1.1 --- Linear Sampling Method --- p.44 / Chapter 3.1.2 --- Strengthened Linear Sampling Method --- p.51 / Chapter 3.1.3 --- Multilevel Linear Sampling Method --- p.58 / Chapter 3.2 --- Numerical Simulations of Second Model --- p.68 / Chapter 3.2.1 --- Contrast Source Inversion Method --- p.68 / Chapter 3.2.2 --- Subspace-based Optimization Method --- p.74 / Chapter 3.2.3 --- Twofold Subspace-based Optimization Method --- p.79 / Chapter 3.2.4 --- Multiple Signal Classification Method --- p.85 / Chapter 3.3 --- Numerical Simulations of Third Model --- p.85 / Chapter 3.3.1 --- Boundary Integral Method --- p.86 / Chapter 3.3.2 --- Generalized Dual Space Indicator Method --- p.89 / Chapter 4 --- Conclusion

Dai, Lipeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 73-75). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.0.1 --- Linear sampling method --- p.2 / Chapter 1.0.2 --- choice of cut-off values --- p.5 / Chapter 1.0.3 --- Underwater image problem --- p.7 / Chapter 2 --- Mathematical justification of LSM --- p.10 / Chapter 2.1 --- Some mathematical preparations --- p.11 / Chapter 2.2 --- Well-posedness of an interior transmission problem --- p.13 / Chapter 2.3 --- Linear sampling method: full aperture --- p.20 / Chapter 2.4 --- Linear sampling method: limited aperture --- p.23 / Chapter 3 --- Strengthened linear sampling method --- p.28 / Chapter 3.1 --- Proof of theorem 1.0.3 --- p.28 / Chapter 3.2 --- Several estimates in theory for strengthened LSM --- p.33 / Chapter 4 --- Underwater imaging problem --- p.38 / Chapter 4.1 --- Boundary integral method --- p.38 / Chapter 4.2 --- Approximation of the Integral Kernel in (4.12) --- p.40 / Chapter 4.3 --- Numerical solution of (4.12) --- p.44 / Chapter 4.4 --- Underwater image problem --- p.45 / Chapter 4.5 --- Imaging scheme without a reference object --- p.48 / Chapter 4.6 --- Numerical examples without a reference object --- p.49 / Chapter 4.7 --- Imaging scheme with a reference object --- p.59 / Chapter 4.8 --- Numerical examples with a reference object --- p.61

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 140-144).

No description available.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--School of Industrial and Systems Engineering, Georgia Institute of Technology, 2004. Directed by Joel S. Sokol. / Vita. Includes bibliographical references (leaves 114-116).