Mathematical Modeling and Computation – serie

Wavelets continue to be powerful mathematical tools that can be used to solve problems for which the Fourier (spectral) method does not perform well or cannot handle. This book is for engineers, applied mathematicians, and other scientists who want to learn about using wavelets to analyze, process, and synthesize images and signals. Applications are described in detail and there are step-by-step instructions about how to construct and apply wavelets. The only mathematically rigorous monograph written by a mathematician specifically for nonspecialists, it describes the basic concepts of these mathematical techniques, outlines the procedures for using them, compares the performance of various approaches, and provides information for problem solving, putting the reader at the forefront of current research.New methods and important topics described in the book include the following:Multidimensional and multifrequency wavelet transforms (including wavelet packets). This approach gives the user more flexibility in applying wavelets to study multidimensional data sets.Spline wavelets with uniform or arbitrary knots on a bounded interval (with methods to construct these wavelets, their duals, as well as all decomposition and reconstruction matrices). This tool allows analysis and synthesis of discrete data on uniform or nonuniform sample sites without any boundary effect.Wavelets as a mathematical tool for waveform matching, signal segmentation, and time-frequency localization as well as effective implementation and fast computation.Procedures to construct all the well-known wavelets and to find their corresponding filter sequences.Detailed comparisons of the most popular wavelets and tables of values for evaluating their filtering performance.

Here is a clearly written introduction to three central areas of inverse problems: inverse problems in electromagnetic scattering theory, inverse spectral theory, and inverse problems in quantum scattering theory. Inverse problems, one of the most attractive parts of applied mathematics, attempt to obtain information about structures by nondestructive measurements. Based on a series of lectures presented by three of the authors, all experts in the field, the book provides a quick and easy way for readers to become familiar with the area through a survey of recent developments in inverse spectral and inverse scattering problems.In the opening chapter, Päivärinta collects the mathematical tools needed in the subsequent chapters and gives references for further study. Colton's chapter focuses on electromagnetic scattering problems. As an application he considers the problem of detecting and monitoring leukemia. Rundell's chapter deals with inverse spectral problems. He describes several exact and algorithmic methods for reconstructing an unknown function from the spectral data. Chadan provides an introduction to quantum mechanical inverse scattering problems. As an application he explains the celebrated method of Gardner, Greene, Kruskal, and Miura for solving nonlinear evolution equations such as the Korteweg_de Vries equation. Each chapter provides full references for further study.

In this translation of the German edition, the authors provide insight into the numerical simulation of fluid flow. Using a simple numerical method as an expository example, the individual steps of scientific computing are presented:The derivation of the mathematical model.The discretization of the model equations.The development of algorithms, - parallelization.Visualization of the computed data. In addition to the treatment of the basic equations for modeling laminar, transient flow of viscous, incompressible fluids - the Navier–Stokes equations - the authors look at the simulation of free surface flows, energy and chemical transport, and turbulence.Detailed hints for the implementation of the various algorithms enable readers to write their own flow simulation program from scratch. The variety of applications is shown in several simulation results, including 92 black-and-white and 18 color illustrations. Moreover, after reading this book, readers should be able to understand more enhanced algorithms of computational fluid dynamics and to apply their new knowledge of modeling, discretization, parallelization, and visualization to other scientific fields, where numerical simulation has established itself, in addition to theoretical investigations and practical experiments, as a new path for uncovering the laws of nature. Among these fields are the examination of elastic solids, combustion, melting and coating processes, and crystal growth, as well as weather prediction.

Here is an overview of modern computational stabilization methods for linear inversion, with applications to a variety of problems in audio processing, medical imaging, tomography, seismology, astronomy, and other areas.Rank-deficient problems involve matrices that are either exactly or nearly rank deficient. Such problems often arise in connection with noise suppression and other problems where the goal is to suppress unwanted disturbances of the given measurements.Discrete ill-posed problems arise in connection with the numerical treatment of inverse problems, where one typically wants to compute information about some interior properties using exterior measurements. Examples of inverse problems are image restoration and tomography, where one needs to improve blurred images or reconstruct pictures from raw data.This book describes, in a common framework, new and existing numerical methods for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties of the algorithms and on the efficiency and reliability of the computations. The setting is that of numerical linear algebra rather than abstract functional analysis, and the theoretical development is complemented with numerical examples and figures that illustrate the features of the various algorithms.

Simulated annealing has proved to be an easy and reliable method for finding optimal values of a problem in cases where there is no road map to possible solutions. Facts, Conjectures, and Improvements for Simulated Annealing offers an introduction to this topic for novices and provides an informative review of the area for the more expert reader. This book brings together for the first time many of the theoretical foundations for improvements to algorithms for global optimization that until now existed only in scattered research articles.The method described in this book operates by simulating the cooling of a (usually fictitious) physical system whose possible energies correspond to the values of the objective function being minimized. The analogy works because physical systems occupy only states with the lowest energy as the temperature is lowered to absolute zero.

Mathematical methods based on the theory of stochastic processes have long been used effectively in telephone traffic modeling. Today's modern network traffic, which is distinctly different from traditional voice traffic, generates challenging mathematical and statistical problems. Stochastic Modeling in Broadband Communications Systems provides a concise overview of stochastic models and mathematical techniques for solving these problems and enhances readers' overall understanding of communications systems. The book also presents an excellent introduction, particularly for students and professionals in probability and applied mathematics, to a huge area of interesting problems and models arising from modern developments in broadband channel transmission systems.The author's clear presentation is based on sound mathematical reasoning, and he has taken special care to make the material easily accessible to readers unable to devote excessive amounts of time to rigorous mathematical detail and generality. Topics include models ranging over different time scales of calls, bursts, and cells; different protocol layers for transport, control, and applications; mechanisms for queuing, collisions, delay, and loss; and the effects of buffering, retransmission, multiplexing, and traffic control. Comprehensive exercises are provided at the end of each chapter.

This book introduces mathematicians to real applications from physiology. Using mathematics to analyze physiological systems, the authors focus on models reflecting current research in cardiovascular and pulmonary physiology. In particular, they present models describing blood flow in the heart and the cardiovascular system as well as the transport of oxygen and carbon dioxide through the respiratory system and a model for baroreceptor regulation.Applied Mathematical Models in Human Physiology is the only book available that analyzes up-to-date models of the physiological system at several levels of detail. Some are simple “real-time” models that can be directly used in larger systems, while others are more detailed “reference” models that show the underlying physiological mechanisms and provide parameters for and validation of simpler models. The book also covers two-dimensional modeling of the fluid dynamics in the heart and its ability to pump, and includes a discussion of modeling wave-propagation throughout the systemic arteries.The models presented can be used as case studies in courses on mathematical modeling or as an inspiration for the study and development of physiological models. The first two chapters of the book provide an excellent introduction to the physiologic concepts necessary for understanding the modeling assumptions and methodologies. The remaining chapters describe six different models of the cardiovascular and pulmonary system. Each model is introduced by a case study and can be studied individually.

Partial differential equations (PDEs) are used to describe a large variety of physical phenomena, from fluid flow to electromagnetic fields, and are indispensable to such disparate fields as aircraft simulation and computer graphics. While most existing texts on PDEs deal with either analytical or numerical aspects of PDEs, this innovative and comprehensive textbook features a unique approach that integrates analysis and numerical solution methods and includes a third component - modeling - to address real-life problems. The authors believe that modeling can be learned only by doing; hence a separate chapter containing 16 user-friendly case studies of elliptic, parabolic, and hyperbolic equations is included and numerous exercises are included in all other chapters.Partial Differential Equations enables readers to deepen their understanding of a topic ubiquitous in mathematics and science and to tackle practical problems. The advent of fast computers and the development of numerical methods have enabled the modern engineer to use a large variety of packages to find numerical approximations to solutions of PDEs. Problems are usually standard and a thorough knowledge of a well-chosen subset of analytical and numerical tools and methodologies is necessary when dealing with real-life problems. When one is dealing with PDEs in practice, it becomes clear that both numerical and analytical treatments of the problem are needed.

Exact and Approximate Modeling of Linear Systems elegantly introduces the behavioural approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations. The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular purpose.This book presents exact subspace-type and approximate optimization-based identification methods, as well as representation-free problem formulations, an overview of solution approaches, and software implementation. Readers will find an exposition of a wide variety of modeling problems starting from observed data. The presented theory leads to algorithms that are implemented in C language and in MATLAB.

The field of mathematical biology is growing rapidly. Questions about infectious diseases, heart attacks, cell signaling, cell movement, ecology, environmental changes, and genomics are now being analyzed using mathematical and computational methods. A Course in Mathematical Biology teaches all aspects of modern mathematical modeling and is specifically designed to introduce undergraduate students to problem solving in the context of biology.Divided into three parts, the book covers basic analytical modeling techniques and model validation methods; introduces computational tools used in the modeling of biological problems; and provides a source of open-ended problems from epidemiology, ecology, and physiology. All chapters include realistic biological examples, and there are many exercises related to biological questions. In addition, the book includes 25 open-ended research projects that can be used by students. The book is accompanied by a Web site that contains solutions to most of the exercises and a tutorial for the implementation of the computational modeling techniques. Calculations can be done in modern computing languages such as Maple, Mathematica, and MATLAB®.

Since the advent of computerized tomography in radiology, many imaging techniques have been introduced in medicine, science, and technology. This book describes the state of the art of the mathematical theory and numerical analysis of imaging. The authors survey and provide a unified view of imaging techniques, provide the necessary mathematical background and common framework, and give a detailed analysis of the numerical algorithms. This book not only reflects the theoretical progress and the growth of the field in the last 10 years but also serves as an excellent reference. It will provide readers with a superior understanding of the mathematical principles behind imaging and will enable them to write state-of-the-art software as a result.Mathematical Methods in Image Reconstruction provides a very detailed description of two-dimensional algorithms. For three-dimensional algorithms, the authors derive exact and approximate inversion formulas for specific imaging devices and describe their algorithmic implementation (which by and large parallels the two-dimensional algorithms). Integral geometry is surveyed as far as is necessary for imaging purposes; imaging techniques based on or related to integral geometry are briefly described in the section on tomography.Some of the applications covered in the book include computerized tomography, magnetic resonance imaging, emission tomography, electron microscopy, ultrasound transmission tomography, industrial tomography, seismic tomography, impedance tomography, and NIR imaging. The authors provide the necessary mathematical background and common mathematical framework needed to understand the book. Knowledge of tomography literature from the 1980s will be useful to the reader.

Mathematical modeling - the ability to apply mathematical concepts and techniques to real-life systems - has expanded considerably over the last decades, making it impossible to cover all of its aspects in one course or textbook. Continuum Modeling in the Physical Sciences provides an extensive exposition of the general principles and methods of this growing field with a focus on applications in the natural sciences. The authors present a thorough treatment of mathematical modeling from the elementary level to more advanced concepts.Most of the chapters are devoted to a discussion of central issues such as dimensional analysis, conservation principles, balance laws, constitutive relations, stability, robustness, and variational methods, and are accompanied by numerous real-life examples. Readers will benefit from the exercises placed throughout the text and the Challenging Problems sections found at the ends of several chapters. The last chapter is devoted to elaborated case studies in polymer dynamics, fiber spinning, water waves, and waveguide optics.

Modern financial mathematics relies on the theory of random processes in time, reflecting the erratic fluctuations in financial markets.This book introduces the fascinating area of financial mathematics and its calculus in an accessible manner geared toward undergraduate students. Using little high-level mathematics, the author presents the basic methods for evaluating financial options and building financial simulations.By emphasizing relevant applications and illustrating concepts with colour graphics, Elementary Calculus of Financial Mathematics presents the crucial concepts needed to understand financial options among these fluctuations. Among the topics covered are the binomial lattice model for evaluating financial options, the Black–Scholes and Fokker–Planck equations, and the interpretation of Ito’s formula in financial applications. Each chapter includes exercises for student practice and the appendices offer MATLAB® and SCILAB code as well as alternate proofs of the Fokker–Planck equation and Kolmogorov backward equation.

Presents cutting-edge developments in the theory and experiments of nonlinear waves. Its comprehensive coverage of analytical methods for nonintegrable systems is the first of its kind. It also covers in great depth analytical methods for integrable equations, and comprehensively describes efficient numerical methods for all major aspects of nonlinear wave computations. In addition, this book presents the latest experiments on nonlinear waves in optical systems and Bose–Einstein condensates, especially in periodic media. The book contains a large number of simple and efficient MATLAB® codes for various nonlinear wave computations, which readers can easily adapt to solve their own problems. The codes can also be found on an associated Web page.

Interesting, real-world mathematical modeling problems are complex and can usually be studied at different scales. The scale at which the investigation is carried out is one of the factors that determines the type of mathematics most appropriate to describe the problem. The book concentrates on two modeling paradigms: the macroscopic, in which the authors describe phenomena in terms of time evolution via ordinary differential equations, and the microscopic, which requires knowledge of random events and probability. The text emphasizes the development of computational skills to construct predictive models and analyze the results. To elucidate the concepts, a wealth of examples and portions of MATLAB? code used by the authors are included.Computational Mathematical Modeling: An Integrated Approach Across Scales:Is designed for classroom use, has been extensively tested by the authors, and has homework problems carefully designed to develop students’ computational skills.Is based on an unorthodox combination of deterministic and probablistic methodologies that are naturally bridged through examples.Painlessly introduces students to advanced themes in a natural progression.Includes suggestions for further reading.

Until recently, measurable dynamics has been held as a highly theoretcal mathematical topic with few generally known obvious links for practitioners in areas of applied mathematics. However, the advent of high-speed computers, rapidly developing algorithms, and new numerical methods has allowed for a tremendous amount of progress and sophistication in efforts to represent the notion of a transfer operator discretely but to high resolution.This book connects many concepts in dynamical systems with mathematical tools from areas such as graph theory and ergodic theory. The authors introduce practical tools for applications related to measurable dynamical systems, coherent structures, and transport problems.The new and fast-developing computational tools discussed throughout the book allow for detailed analysis of real-world problems that are simply beyond the reach of traditional methods.

Climate modeling and simulation teach us about past, present, and future conditions of life on earth and help us understand observations about the changing atmosphere and ocean and terrestrial ecology.Focusing on high-end modeling and simulation of earth's climate, Climate Modeling for Scientists and Engineers:Presents observations about the general circulations of the earth and the partial differential equations used to model the dynamics of weather and climate.Covers numerical methods for geophysical flows in more detail than many other texts.Discusses parallel algorithms and the role of high-performance computing used in the simulation of weather and climate.Provides supplemental lectures and MATLAB? exercises on an associated Web page.

Arising out of the growing interest in and applications of modern dynamical systems theory, this book explores how to derive relatively simple dynamical equations that model complex physical interactions. The author’s objectives are to use sound theory to explore algebraic techniques; develop interesting applications; and discover general modeling principles.Model Emergent Dynamics in Complex Systems:Unifies into one powerful and coherent approach the many varied extant methods for mathematical model reduction and approximation.Uses mathematical models at various levels of resolution and complexity—it establishes the relationships between such multiscale models and clarifies difficulties and apparent paradoxes. Addresses model reduction for systems, resolves initial conditions, and illuminates control and uncertainty.

This book provides an overview of the myriad methods for applying dynamical systems techniques to PDEs and highlights the impact of PDE methods on dynamical systems. Also included are many nonlinear evolution equations, which have been benchmark models across the sciences, and examples and techniques to strengthen preparation for research. PDE Dynamics: An Introduction is intended for senior undergraduate students, beginning graduate students, and researchers in applied mathematics, theoretical physics, and adjacent disciplines. Structured as a textbook or seminar reference, it can be used in courses titled Dynamics of PDEs, PDEs 2, Dynamical Systems 2, Evolution Equations, or Infinite-Dimensional Dynamics.