Maths for engineers Books

Anthony Croft is Professor of Mathematics Education at Loughborough University. Robert Davison was formerly Head of Quality at the Faculty of Technology, De Montfort University. Martin Hargreaves is a Chartered Physicist James Flint is Senior Lecturer in Wireless Systems Engineering at Loughborough University.

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Book SynopsisThis self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.Trade Review'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website …' Volker H. Schulz, SIAM ReviewTable of Contents1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.

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Book SynopsisA long-standing, best-selling, comprehensive textbook covering all the mathematics required on upper level engineering mathematics undergraduate courses. Its unique approach takes you through all the mathematics you need in a step-by-step fashion with a wealth of examples and exercises. The text demands that you engage with it by asking you to complete steps that you should be able to manage from previous examples or knowledge you have acquired, while carefully introducing new steps. By working with the authors through the examples, you become proficient as you go. By the time you come to trying examples on their own, confidence is high. Suitable for undergraduates in second and third year courses on engineering and science degrees.Trade ReviewQuite simply absolutely excellent! All you could want in a maths textbook. Stroud does not “assume”, he teaches. * What students say *Table of ContentsHints on Using the Book Useful Background Information Numerical Solutions of Equations and Interpolation Laplace Transforms Part 1 Laplace Transforms Part 2 Laplace Transforms Part 3 Difference Equations and the Z Transform Introduction to Invariant Linear Systems Fourier Series 1 Fourier Series 2 Introduction to the Fourier Transform Power Series Solutions of Ordinary Differential Equations 1 Power Series Solutions of Ordinary Differential Equations 2 Power Series Solutions of Ordinary Differential Equations 3 Numerical Solutions of Ordinary Differential Equations Matrix Algebra Systems of Ordinary Differential Equations Direction Fields Phase Plane Analysis Non-linear Systems Dynamical Systems Partial Differentiation Partial Differential Equations Numerical Solutions of Partial Differential Equations Multiple Integration Part 1 Multiple Integration Part 2 Integral Functions Vector Analysis Part 1 Vector Analysis Part 2 Vector Analysis Part 3 Complex Analysis Part 1 Complex Analysis Part 2 Complex Analysis Part 3 Optimization and Linear Programming.

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Book SynopsisAn understanding of the extended finite element method (XFEM) is critical for users, developers, researchers, and engineers working on industrial products. The first guide to the foundations of XFEM and its implementation, this book demystifies the theory behind this method and makes it accessible to anyone with previous knowledge of FEM.Table of ContentsList of Contributors xi Preface xiii Acknowledgments xv 1 Introduction 1 1.1 The Finite Element Method 2 1.2 Suitability of the Finite Element Method 9 1.3 Some Limitations of the FEM 11 1.4 The Idea of Enrichment 16 1.5 Conclusions 19 2 A Step-by-Step Introduction to Enrichment 23 2.1 History of Enrichment for Singularities and Localized Gradients 25 2.2 Weak Discontinuities for One-dimensional Problems 38 2.3 Strong Discontinuities for One-dimensional Problem 58 2.4 Conclusions 61 3 Partition of Unity Revisited 67 3.1 Completeness, Consistency, and Reproducing Conditions 67 3.2 Partition of Unity 68 3.3 Enrichment 69 3.4 Numerical Examples 86 3.5 Conclusions 95 4 Advanced Topics 99 4.1 Size of the Enrichment Zone 99 4.2 Numerical Integration 100 4.3 Blending Elements and Corrections 108 4.4 Preconditioning Techniques 116 5 Applications 125 5.1 Linear Elastic Fracture in Two Dimensions with XFEM 125 5.2 Numerical Enrichment for Anisotropic Linear Elastic Fracture Mechanics 130 5.3 Creep and Crack Growth in Polycrystals 133 5.4 Fatigue Crack Growth Simulations 138 5.5 Rectangular Plate with an Inclined Crack Subjected to Thermo-Mechanical Loading 140 6 Recovery-Based Error Estimation and Bounding in XFEM 145 6.1 Introduction 145 6.2 Error Estimation in the Energy Norm. The ZZ Error Estimator 147 6.3 Recovery-based Error Estimation in XFEM 151 6.4 Recovery Techniques in Error Bounding. Practical Error Bounds. 174 6.5 Error Estimation in Quantities of Interest 179 7 Φ-FEM: An Efficient Simulation Tool Using Simple Meshes for Problems in Structure Mechanics and Heat Transfer 191 7.1 Introduction 191 7.2 Linear Elasticity 194 7.3 Linear Elasticity with Multiple Materials 204 7.4 Linear Elasticity with Cracks 208 7.5 Heat Equation 212 7.6 Conclusions and Perspectives 214 8 eXtended Boundary Element Method (XBEM) for Fracture Mechanics and Wave Problems 217 8.1 Introduction 217 8.2 Conventional BEM Formulation 218 8.3 Shortcomings of the Conventional Formulations 226 8.4 Partition of Unity BEM Formulation 228 8.5 XBEM for Accurate Fracture Analysis 228 8.6 XBEM for ShortWave Simulation 235 8.7 Conditioning and its Control 243 8.8 Conclusions 245 9 Combined Extended Finite Element and Level Set Method (XFE-LSM) for Free Boundary Problems 249 9.1 Motivation 249 9.2 The Level Set Method 250 9.3 Biofilm Evolution 256 9.4 Conclusion 269 10 XFEM for 3D Fracture Simulation 273 10.1 Introduction 273 10.2 Governing Equations 274 10.3 XFEM Enrichment Approximation 275 10.4 Vector Level Set 280 10.5 Computation of Stress Intensity Factor 282 10.6 Numerical Simulations 288 10.7 Summary 300 11 XFEM Modeling of Cracked Elastic-Plastic Solids 303 11.1 Introduction 303 11.2 Conventional von Mises Plasticity 303 11.3 Strain Gradient Plasticity 312 11.4 Conclusions 323 12 An Introduction to Multiscale analysis with XFEM 329 12.1 Introduction 329 12.2 Molecular Statics 330 12.3 Hierarchical Multiscale Models of Elastic Behavior -- The Cauchy-Born Rule 336 12.4 Current Multiscale Analysis -- The Bridging Domain Method 338 12.5 The eXtended Bridging Domain Method 340 References 344 Index 345

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Book SynopsisComprehensive, classic introduction to space-flight engineering for advanced undergraduate and graduate students provides basic tools for quantitative analysis of the motions of satellites and other vehicles in space.

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Book SynopsisReplaces the contrived application of the quantum master equation with a satisfactory treatment of quantum fluctuations. This work covers the fluctuations and stochastic methods for describing them. It is of interest to students and researchers in applied mathematics, physics and physical chemistry.Table of ContentsI. Stochastic variablesII. Random eventsIII. Stochastic processesIV. Markov processesV. The master equationVI. One-step processesVII. Chemical reactionsVIII. The Fokker-Planck equationIX. The Langevin approachX. The expansion of the master equationXI. The diffusion typeXII. First-passage problemsXIII. Unstable systemsXIV. Fluctuations in continuous systemsXV. The statistics of jump eventsXVI. Stochastic differential equationsXVII. Stochastic behavior of quantum systems

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Book SynopsisThe eighth edition of Chapra and Canale''s Numerical Methods for Engineers retains the instructional techniques that have made the text so successful. The book covers the standard numerical methods employed by both students and practicing engineers. Although relevant theory is covered, the primary emphasis is on how the methods are applied for engineering problem solving. Each part of the book includes a chapter devoted to case studies from the major engineering disciplines. Numerous new or revised end-of chapter problems and case studies are drawn from actual engineering practice. This edition also includes several new topics including a new formulation for cubic splines, Monte Carlo integration, and supplementary material on hyperbolic partial differential equations.Table of ContentsPart 1 - Modeling, Computers, and Error Analysis1) Mathematical Modeling and Engineering Problem Solving2) Programming and Software3) Approximations and Round-Off Errors4) Truncation Errors and the Taylor SeriesPart 2 - Roots of Equations5) Bracketing Methods6) Open Methods7) Roots of Polynomials8) Case Studies: Roots of EquationsPart 3 - Linear Algebraic Equations9) Gauss Elimination10) LU Decomposition and Matrix Inversion11) Special Matrices and Gauss-Seidel12) Case Studies: Linear Algebraic EquationsPart 4 - Optimization13) One-Dimensional Unconstrained Optimization14) Multidimensional Unconstrained Optimization15) Constrained Optimization16) Case Studies: OptimizationPart 5 - Curve Fitting17) Least-Squares Regression18) Interpolation19) Fourier Approximation20) Case Studies: Curve FittingPart 6 - Numerical Differentiation and Integration21) Newton-Cotes Integration Formulas22) Integration of Equations23) Numerical Differentiation24) Case Studies: Numerical Integration and DifferentiationPart 7 - Ordinary Differential Equations25) Runge-Kutta Methods26) Stiffness and Multistep Methods27) Boundary-Value and Eigenvalue Problems28) Case Studies: Ordinary Differential EquationsPart 8 - Partial Differential Equations29) Finite Difference: Elliptic Equations30) Finite Difference: Parabolic Equations31) Finite-Element Method32) Case Studies: Partial Differential EquationsAppendix A - The Fourier SeriesAppendix B - Getting Started with MatlabAppendix C - Getting Starte dwith MathcadBibliographyIndex

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Book SynopsisUse this book to nudge your brain~mind into its metastable mode again and again, to better perceive the complementary dances of contraries, and to transcend the detrimental narrow-mindedness of polarized, either/or thinking.

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Book SynopsisEnsure students are fully prepared for A-Level Maths with this revised second edition, fully updated to bridge the GCSE Maths 9-1 and A-level 2017 specifications.Written by an experienced A-level author who is a practising A-level teacher, this fully updated edition is an ideal resource to be used in the classroom or for independent study.Similar in structure to Collins Maths revision guides, the Bridging GCSE and A-level Maths Student Book is split into an explanation section and a practice section. Identify and understand the transition from GCSE to AS and A-level Maths with What you should already know' objectives and What you will learn' objectives at the start of each topic Get a head start on your AS/A-level Maths with introductions to key pure maths topics for all exam boards (AQA, OCR, MEI and Edexcel) Boost your understanding with worked examples which include extra guidance in the form of Handy hint', Checkpoint', A-level Alert!' and Common error' boxes Reinforce and build onTrade ReviewThe transition between GCSE and AS/A-level mathematics is considerable. This essential textbook will help students navigate their way through all the key concepts and applications to enable students to achieve real success in post-16 mathematics. Chris CurtisHead of MathematicsFrome Community College

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Book SynopsisConfusing Textbooks? Missed Lectures? Not Enough Time?Fortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum's Outline gives you Practice problems with full explanations that reinforce knowledge Coverage of the most up-to-date developments in your course field In-depth review of practices and applications Fully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores!Schaum's Outlines-Problem Solved.Table of ContentsSchaum's Outline of Basic Mathematics with Applications to Science and Technology, 2ed 1. Decimal Fractions 2. Measurement and Scientific Notation 3. Common Fractions 4. Percentage 5. Essentials of Algebra 6. Ratio and Proportion 7. Linear Equations 8. Exponents and Radicals 9. Logarithms 10. Quadratic Equations and Square Roots 11. Essentials of Plane Geometry 12. Solid Figures 13. Trigonometric Figures 14. Solution of Triangles 15. Vectors 16. Radian Measure 17. Conic Sections 18. Numbering Systems 19.Arithmetic Operations in a Computer 20.Counting Methods 21.Probability and Odds 22.Statistics

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Book SynopsisTrade Review"Overall, the reviewer considers this text to offer a good and useful coverage of advanced mathematics for engineers. It gives useful and succinct coverage of the topics included." --IEEE PulseTable of Contents1. Prologue 2. Ordinary Differential Equations 3. Laplace and Fourier Transform Methods 4. Matrices and Linear Systems of Equations 5. Analytical Methods for Solving Partial Differential Equations 6.Difference Numerical Methods for Differential Equations 7. Finite Element Technique 8. Treatment of Experimental Results 9. Numerical Analysis 10. Introduction to Complex Analysis 11. Nondimensionalisation 12. Nonlinear Differential Equations 13. Integral Equations 14. Calculus of Variations

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Book SynopsisTable of ContentsCHAPTER 1 Introduction to statistics CHAPTER 2 Descriptive statistics CHAPTER 3 Elements of probability CHAPTER 4 Random variables and expectation CHAPTER 5 Special random variables CHAPTER 6 Distributions of sampling statistics CHAPTER 7 Parameter estimation CHAPTER 8 Hypothesis testing CHAPTER 9 Regression CHAPTER 10 Analysis of variance CHAPTER 11 Goodness of fit tests and categorical data analysis CHAPTER 12 Nonparametric hypothesis tests CHAPTER 13 Quality control CHAPTER 14 Life testing CHAPTER 15 Simulation, bootstrap statistical methods, and permutation tests CHAPTER 16 Machine learning and big data

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Book SynopsisThis book gives a broad range of worked mathematical examples which are appropriate for scientists and engineers, ranging from basic algebra to calculus and Fourier transforms. Together with its companion volume Foundations of Science Mathematics (Oxford Chemistry Primer 77), it summarizes the basic concepts and results that should be familiar from high school, and then extends the ideas to cover the material needed by the majority of scienceundergraduates.Table of Contents1. Basic algebra and arithmetic ; 2. Curves and graphs ; 3. Trigonometry ; 4. Differentiation ; 5. Integration ; 6. Taylor series ; 7. Complex numbers ; 8. Vectors ; 9. Matrices ; 10. Partial differentiation ; 11. Line integrals ; 12. Multiple integrals ; 13. Ordinary differential equations ; 14. Partial differential equations ; 15. Fourier series and transforms

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Book SynopsisThis book is a new edition of Tensors and Manifolds: With Applications to Mechanics and Relativity which was published in 1992. It is based on courses taken by advanced undergraduate and beginning graduate students in mathematics and physics, giving an introduction to the expanse of modern mathematics and its application in modern physics. It aims to fill the gap between the basic courses and the highly technical and specialised courses which both mathematics and physics students require in their advanced training, while simultaneously trying to promote, at an early stage, a better appreciation and understanding of each other''s discipline. The book sets forth the basic principles of tensors and manifolds, describing how the mathematics underlies elegant geometrical models of classical mechanics, relativity and elementary particle physics. The existing material from the first edition has been reworked and extended in some sections to provide extra clarity, as well as additional problemTrade ReviewReview from previous edition Clearly written and self-contained and, in particular, the author has succeeded in combining mathematical rigor with a certain degree of informality in a satisfactory way. As such, this work will certainly be appreciated by a wide audience. * Mathematical Reviews, August 1993 *Table of Contents1. Vector spaces ; 2. Multilinear mappings and dual spaces ; 3. Tensor product spaces ; 4. Tensors ; 5. Symmetric and skew-symmetric tensors ; 6. Exterior (Grassmann) algebra ; 7. The tangent map of real cartesian spaces ; 8. Topological spaces ; 9. Differentiable manifolds ; 10. Submanifolds ; 11. Vector fields, 1-forms and other tensor fields ; 12. Differentiation and integration of differential forms ; 13. The flow and the Lie derivative of a vector field ; 14. Integrability conditions for distributions and for pfaffian systems ; 15. Pseudo-Riemannian manifolds ; 16. Connection 1-forms ; 17. Connection on manifolds ; 18. Mechanics ; 19. Additional topics in mechanics ; 20. A spacetime ; 21. Some physics on Minkowski spacetime ; 22. Einstein spacetimes ; 23. Spacetimes near an isolated star ; 24. Nonempty spacetimes ; 25. Lie groups ; 26. Fiber bundles ; 27. Connections on fiber bundles ; 28. Gauge theory

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Book SynopsisThis monograph provides a treatment of the theory of algebraic Riccati equations, an area of increasing interest in the mathematics and engineering communities. A range of applications are covered, demonstrating the use of these equations for providing solutions to complex problems.Table of Contents1. Preliminaries from the theory of matrices ; 2. Indefinite scalar products ; 3. Skew-symmetric scalar products ; 4. Matrix theory and control ; 5. Linear matrix equations ; 6. Rational matrix functions ; 7. Geometric theory: the complex case ; 8. Geometric theory: the real case ; 9. Constructive existence and comparison theorems ; 10. Hermitian solutions and factorizations of rational matrix functions ; 11. Perturbation theory ; 12. Geometric theory for the discrete algebraic Riccati equation ; 13. Constructive existence and comparison theorems ; 14. Perturbation theory for discrete algebraic Riccati equations ; 15. Discrete algebraic Riccati equations and matrix pencils ; 16. Linear-quadratic regulator problems ; 17. The discrete Kalman filter ; 18. The total least squares technique ; 19. Canonical factorization ; 20. Hoo control problems ; 21. Contractive rational matrix functions ; 22. The matrix sign function ; 23. Structured stability radius ; Bibliography ; List of notations ; Index

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Book SynopsisThis book is aimed at those readers who already have some knowledge of mathematical methods and have also been introduced to the basic ideas of quantum optics. It should be attractive to students who have already explored one of the more introductory texts such as Loudon''s The quantum theory of light (2/e, 1983, OUP) and are seeking to acquire the mathematical skills used in real problems. This book is not primarily about the physics of quantum optics but rather presents the mathematical methods widely used by workers in this field. There is no comparable book which covers either the range or the depth of mathematical techniques.Trade Review... the authors are well-known for their work on topics, such as the quantum-phase operator and quasi-probability distribution so theory PhD students will be able to learn these subjects direct from the horse's mouth. The authors have [] included, for pedagogic purposes, extra detail of the mathematical workings that a PhD student would not be able find in the research literature. * New Scientist, 6 June 1998 *The reader will find here a very clear presentation of material not readily found elsewhere. Postgraduate students of quantum optics will find this work to be of the greatest utility... Care has been taken to present quite difficult topics in the simplest and most straightforward way; and yet the treatment is concise and focused... Experienced researchers will find that this text is a most convenient handbook of techniques, and will want it close to their elbow. * Contemporary Physics, 1998, vol. 39, no. 4 *Table of Contents1. Foundations ; 2. Coherent interactions ; 3. Operators and states ; 4. Quantum statistics of fields ; 5. Dissipative processes ; 6. Dressed states ; Appendices ; Selected bibliography ; Index

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Book SynopsisThis monograph explores the equivalence of signal functions with their sets of values taken at discrete points. Beginning with an introduction to the main ideas, and background material on Fourier analysis and Hilbert spaces and their bases, it covers a wide variety of topics.Trade Review...the text is written by use of LATEX and its beautiful graphics reveal the power and the advantages of this system. * Zentralblatt fuer Mathematik 827/97 *Table of Contents1. An introduction to sampling theory ; 1.1 General introduction ; 1.2 Introduction - continued ; 1.3 The seventeenth to the mid twentieth century - a brief review ; 1.4 Interpolation and sampling from the seventeenth century to the mid twentieth century - a brief review ; 1.5 Introduction - concluding remarks ; 2. Background in Fourier analysis ; 2.1 The Fourier Series ; 2.2 The Fourier transform ; 2.3 Poisson's summation formula ; 2.4 Tempered distributions - some basic facts ; 3. Hilbert spaces, bases and frames ; 3.1 Bases for Banach and Hilbert spaces ; 3.2 Riesz bases and unconditional bases ; 3.3 Frames ; 3.4 Reproducing kernel Hilbert spaces ; 3.5 Direct sums of Hilbert spaces ; 3.6 Sampling and reproducing kernels ; 4. Finite sampling ; 4.1 A general setting for finite sampling ; 4.2 Sampling on the sphere ; 5. From finite to infinite sampling series ; 5.1 The change to infinite sampling series ; 5.2 The Theorem of Hinsen and Kloosters ; 6. Bernstein and Paley-Weiner spaces ; 6.1 Convolution and the cardinal series ; 6.2 Sampling and entire functions of polynomial growth ; 6.3 Paley-Weiner spaces ; 6.4 The cardinal series for Paley-Weiner spaces ; 6.5 The space ReH1 ; 6.6 The ordinary Paley-Weiner space and its reproducing kernel ; 6.7 A convergence principle for general Paley-Weiner spaces ; 7. More about Paley-Weiner spaces ; 7.1 Paley-Weiner theorems - a review ; 7.2 Bases for Paley-Weiner spaces ; 7.3 Operators on the Paley-Weiner space ; 7.4 Oscillatory properties of Paley-Weiner functions ; 8. Kramer's lemma ; 8.1 Kramer's Lemma ; 8.2 The Walsh sampling therem ; 9. Contour integral methods ; 9.1 The Paley-Weiner theorem ; 9.2 Some formulae of analysis and their equivalence ; 9.3 A general sampling theorem ; 10. Ireggular sampling ; 10.1 Sets of stable sampling, of interpolation and of uniqueness ; 10.2 Irregular sampling at minimal rate ; 10.3 Frames and over-sampling ; 11. Errors and aliasing ; 11.1 Errors ; 11.2 The time jitter error ; 11.3 The aliasing error ; 12. Multi-channel sampling ; 12.1 Single channel sampling ; 12.3 Two channels ; 13. Multi-band sampling ; 13.1 Regular sampling ; 13.3 An algorithm for the optimal regular sampling rate ; 13.4 Selectively tiled band regions ; 13.5 Harmonic signals ; 13.6 Band-ass sampling ; 14. Multi-dimensional sampling ; 14.1 Remarks on multi-dimensional Fourier analysis ; 14.2 The rectangular case ; 14.3 Regular multi-dimensional sampling ; 15. Sampling and eigenvalue problems ; 15.1 Preliminary facts ; 15.2 Direct and inverse Sturm-Liouville problems ; 15.3 Further types of eigenvalue problem - some examples ; 16. Campbell's generalised sampling theorem ; 16.1 L.L. Campbell's generalisation of the sampling theorem ; 16.2 Band-limited functions ; 16.3 Non band-limited functions - an example ; 17. Modelling, uncertainty and stable sampling ; 17.1 Remarks on signal modelling ; 17.2 Energy concentration ; 17.3 Prolate Spheroidal Wave functions ; 17.4 The uncertainty principle of signal theory ; 17.5 The Nyquist-Landau minimal sampling rate

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Book SynopsisThis book gives a detailed mathematical and statistical analysis of the cointegrated vector autoregresive model. This model had gained popularity because it can at the same time capture the short-run dynamic properties as well as the long-run equilibrium behaviour of many non-stationary time series. It also allows relevant economic questions to be formulated in a consistent statistical framework.Part I of the book is planned so that it can be used by those who want to apply the methods without going into too much detail about the probability theory. The main emphasis is on the derivation of estimators and test statistics through a consistent use of the Guassian likelihood function. It is shown that many different models can be formulated within the framework of the autoregressive model and the interpretation of these models is discussed in detail. In particular, models involving restrictions on the cointegration vectors and the adjustment coefficients are discussed, as well as the role

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Book SynopsisMeasurement is a fundamental concept that underpins almost every aspect of the modern world. It is central to the sciences, social sciences, medicine, and economics, but it affects everyday life. We measure everything - from the distance of far-off galaxies to the temperature of the air, levels of risk, political majorities, taxes, blood pressure, IQ, and weight. The history of measurement goes back to the ancient world, and its story has been one of gradual standardization. Today there are different types of measurement, levels of accuracy, and systems of units, applied in different contexts. Measurement involves notions of variability, accuracy, reliability, and error, and challenges such as the measurement of extreme values.In this Very Short Introduction, David Hand explains the common mathematical framework underlying all measurement, the main approaches to measurement, and the challenges involved. Following a brief historical account of measurement, he discusses measurement as used in the physical sciences and engineering, the life sciences and medicine, the social and behavioural sciences, economics, business, and public policy.ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.Table of ContentsREFERENCES; FURTHER READING; INDEX

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Book SynopsisThe Chemistry Maths Book provides a complete course companion suitable for students at all levels. All the most useful and important topics are covered, with numerous examples of applications in chemistry and the physical sciences. Taking a clear, straightforward approach, the book develops ideas in a logical, coherent way, allowing students progressively to build a thorough working understanding of the subject.Topics are organized into three parts: algebra, calculus, differential equations, and expansions in series; vectors, determinants and matrices; and numerical analysis and statistics. The extensive use of examples illustrates every important concept and method in the text, and are used to demonstrate applications of the mathematics in chemistry and several basic concepts in physics. The exercises at the end of each chapter, are an essential element of the development of the subject, and have been designed to give students a working understanding of the material in the text.Online Resources:The online resources feature the following: - Figures from the book in electronic format, ready to download- Full worked solutions to all end of chapter exercisesTrade ReviewReview from previous edition It seems well suited both for its stated purpose and as a "brush-up" book for undergraduates, graduate students, and others. The mathematics are carried out briskly and with very little dressing ... there is much material to cover here and it works well through Steiner's particularly lucid presentation. The notation is standard and clear ... I am impressed with this book, I am sure that it will remain open on my desk and will become well worn in short order. * C. Michael McCallum, University of the Pacific, Journal of Chemical Education, Vol. 74 No. 12 December 1997 *Table of Contents1. Numbers, variables and units ; 2. Algebraic functions ; 3. Transcendental functions ; 4. Differentiation ; 5. Integration ; 6. Methods of integration ; 7. Sequences and series ; 8. Complex numbers ; 9. Functions of several variables ; 10. Functions in 3 dimensions ; 11. First-order differential equations ; 12. Second-order differential equations. Constant coefficients ; 13. Second-order differential equations. Some special functions ; 14. Partial differential equations ; 15. Orthogonal expansions. Fourier analysis ; 16. Vectors ; 17. Determinants ; 18. Matrices and linear transformations ; 19. The matrix eigenvalue problem ; 20. Numerical methods ; 21. Probability and statistics

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Book SynopsisApplying Maths in the Chemical and Biomolecular Sciences uses an extensive array of examples to demonstrate how mathematics is applied to probe and understand chemical and biological systems. It also embeds the use of software, showing how the application of maths and use of software now go hand-in-hand.Trade ReviewIt is particularly useful for Scientists applying mathematical techniques to their analyses. However, professionals in other fields (e.g. Economics and Finance) will also find the mathematical techniques highly useful and relevant. I myself am an Economist, and found the mathematical content very good, and the scientific applications very illuminating. * reviewer on Amazon.com *Very well written and the perfect resource for self study. * The Higher Education Academy Physical Sciences Centre *Table of ContentsAPPENDIX 1: A MAPLE LANGUAGE CRIB

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Book SynopsisMathematical Techniques provides a complete course in mathematics, covering all the essential topics with which a physical sciences or engineering student should be familiar. It introduces and builds on concepts in a progressive, carefully-layered way, and features over 2000 end of chapter problems, plus additional self-check questions.Trade ReviewReview from previous edition This textbook offers an accessible and comprehensive grounding in many of the mathematical techniques required in the early stages of an engineering or science degree and also for the routine methods needed by first and second year mathematics students. * Engineering Designer March/April 2003 *There are also significant changes in content in the opening chapter, where the foundation material has been expanded usefully. The authors do not attempt to dodge theoretical hurdles. They are careful to explain many of the less intuitive properties of functions and to highlight generalisations without becoming over abstract. * Times Higher Education Supplement, November 2002 *Thoroughly recommended. * Zentralblatt MATH, 993:2002 *Table of ContentsPART 1. ELEMENTARY METHODS, DIFFERENTIATION, COMPLEX NUMBERS; PART 2. MATRIX AND VECTOR ALGEBRA; PART 3. INTEGRATION AND DIFFERENTIAL EQUATIONS; PART 4. TRANSFORMS AND FOURIER SERIES; PART 5. MULTIVARIABLE CALCULUS; PART 6. DISCRETE MATHEMATICS; PART 7. PROBABILITY AND STATISTICS; PART 8. PROJECTS; SELF-TESTS: SELECTED ANSWERS; ANSWERS TO SELECTED PROBLEMS; APPENDICES; FURTHER READING; INDEX

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Book SynopsisBased on course material used by the author at Yale University, this practical text addresses the widening gap found between the mathematics required for upper-level courses in the physical sciences and the knowledge of incoming students.Trade Review`Shankar obviously enjoys his mathematics, and his attitude toward mathematics is simultaneously refreshing and contagious....Dirac notation is intriguingly introduced in the discussion of vector spaces. Finally, the book is richly endowed with well-chosen problems.' American Journal of Physics `Consistent with the needs of science students...a sound mathematical reference for anyone studying or practicing in the physical sciences.' Choice Table of ContentsDifferential Calculus of One Variable. Integral Calculus. Calculus of Many Variables. Infinite Series. Complex Numbers. Functions of a Complex Variable. Vector Calculus. Matrices and Determinants. Linear Vector Spaces. Differential Equations. Answers. Index.

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Book SynopsisTable of Contents1. Introduction 2. Harmonic Balance Method and Time Domain Collocation Method 3. Dealiasing for Harmonic Balance and Time Domain Collocation Methods 4. Application of Time Domain Collocation in Formation Flying of Satellites 5. Local Variational Iteration Method 6. Collocation of Local Variational Iteration Method 7. Application of Local Variational Iteration Method in Orbital Mechanics 8. Applications of Local Variational Iteration Method in Structural Dynamics

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Book SynopsisPETER TURNER is a Professor in the Department of Mathematics at the US Naval Academy in Annapolis

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Book SynopsisComputer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.Trade ReviewFrom the reviews:"The book is composed of 22 chapters. … This is a very readable book. … Kay’s book undoubtedly will see its greatest use in engineering schools, but I think it would work nicely in other settings as well. … It is written in a clear and informal style that students will appreciate, its coverage is excellent, and the author’s stated objective (to lessen the difficulty that students usually experience assimilating and applying probability and random processes) will, I predict, be met." (Ralph P. Russo, The American Statistician, Vol. 62 (2), May, 2008)“Kay’s book occupies a unique place in the overcrowded market of textbooks on probability and random processes. … This new textbook is a breath of fresh air in the market of books devoted to probability and random processes. The book lives up to its ambition of setting a new standard for a modern, computer-based treatment of the subject. … I fully recommend its use in undergraduate and first-year graduate courses.” (Osvaldo Simeone, IEEE Control Systems Magazine, Vol. 27, June, 2007)Table of ContentsComputer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-Dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-Dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.

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Book SynopsisThis book provides an introduction to MIP modeling and to planning systems, a unique collection of reformulation results, and an easy to use problem-solving library. This approach is demonstrated through a series of real life case studies, exercises and detailed illustrations.Trade ReviewFrom the reviews: "The book provides a complete overview of different models existing in the literature as well as in practice. … The authors also analyze MIP (mixed integer programming) based algorithms … . Practitioners who are interested in using MIP … can use the book to identify the most efficient way to formulate the problems and to choose the most efficient solution method. … it also can serve as a good reference for students and researchers. Overall, this is an excellent book." (Panos M. Pardalos, Mathematical Reviews, Issue 2006 k) "Recently published Production Planning by Mixed Integer Programming by Yves Pochet and Laurence Wolsey has raised considerable expectations. Firstly, problems of production planning are among the most interesting in Operations Research. … Secondly, both authors are renowned experts in the field. … There is no doubt that this volume offers the present best introduction to integer programming formulations of lot-sizing problems, encountered in production planning." (Jakub Marecek, The Computer Journal, September, 2007)Table of ContentsProduction Planning and MIP.- The Modeling and Optimization Approach.- Production Planning Models and Systems.- Mixed Integer Programming Algorithms.- Classification and Reformulation.- Reformulations in Practice.- Basic Polyhedral Combinatorics for Production Planning and MIP.- Mixed Integer Programming Algorithms and Decomposition Approaches.- Single-Item Uncapacitated Lot-Sizing.- Basic MIP and Fixed Cost Flow Models.- Single-Item Lot-Sizing.- Lot-Sizing with Capacities.- Backlogging and Start-Ups.- Single-Item Variants.- Multi-Item Lot-Sizing.- Multi-Item Single-Level Problems.- Multi-Level Lot-Sizing Problems.- Problem Solving.- Test Problems.

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Book SynopsisRigid Body Dynamics Algorithms presents the subject of computational rigid-body dynamics through the medium of spatial 6D vector notation. The use of spatial vector notation facilitates the implementation of dynamics algorithms on a computer: shorter, simpler code that is easier to write, understand and debug, with no loss of efficiency.Trade ReviewFrom the reviews: "This book deals with a numerical treatment of different problems in the dynamics of rigid-body systems which arise mainly in robotics ... . is centered on mechanical models made up of many rigid bodies connected by joints. ... The book is written in a clear way. Each chapter begins by stating the objectives to be achieved. The algorithms presented are well documented and worked examples are also given. ... the bibliography close this useful book on the computational approach to the dynamics of rigid-body systems." (A. San Miguel, Mathematical Reviews, Issue 2011 h)Table of ContentsIntroduction.- Spatial Vector Algebra.- Dynamics of Rigid Body Systems.- Modelling Rigid Body Systems.- Inverse Dynamics.- Forward Dynamics - Inertia Matrix Methods.- Forward Dynamics - Propagation Methods.- Closed Loop Systems.- Hybrid Dynamics and Other Topics.- Accuracy and Efficiency.- Contact and Impact.

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Book SynopsisExamples.- A Model for Longitudinal Data.- Exploratory Data Analysis.- Estimation of the Marginal Model.- Inference for the Marginal Model.- Inference for the Random Effects.- Fitting Linear Mixed Models with SAS.- General Guidelines for Model Building.- Exploring Serial Correlation.- Local Influence for the Linear Mixed Model.- The Heterogeneity Model.- Conditional Linear Mixed Models.- Exploring Incomplete Data.- Joint Modeling of Measurements and Missingness.- Simple Missing Data Methods.- Selection Models.- Pattern-Mixture Models.- Sensitivity Analysis for Selection Models.- Sensitivity Analysis for Pattern-Mixture Models.- How Ignorable Is Missing At Random ?.- The Expectation-Maximization Algorithm.- Design Considerations.- Case Studies.Trade ReviewFrom the reviews: MATHEMATICAL REVIEWS "This book emphasizes practice rather than mathematical rigor and the majority of the chapters are explanatory rather than research oriented. In this respect, guidance and advice on practical issues are the main focus of the text. Hence it will be of interest to applied statisticians and biomedical researchers in industry, particularly in the pharmaceutical industry, medical public health organizations, contract research organizations, and academia." "This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Over 125 illustrations are included in the book. … I do believe that the book may serve as a useful reference to a broader audience. Since practical examples are provided as well as discussion of the leading software utilization, it may also be appropriate as a textbook in an advanced undergraduate-level or a graduate-level course in an applied statistics program." (Ana Ivelisse Avil és, Technometrics, Vol. 43 (3), 2001) "A practical book with a great many examples, including worked computer code and access to the datasets. … The authors state that the book covers ‘linear mixed models for continuous outcomes’ … . The book has four main strengths: its practical bent, its emphasis on exploratory analysis, its description of tools for model checking, and its treatment of dropout and missingness … . my impression of the book was … positive. Its strong practical nature and emphasis on dropout modelling are particularly welcome … ." (Harry Southworth, ISCB Newsletter, June, 2002) "This book is devoted to linear mixed-effects models with strong emphasis on the SAS procedure. Guidance and advice on practical issues are the main focus of the text. … It is of value to applied statisticians and biomedical researchers. … I recommend this book as a reference to applied statisticians and biomedical researchers, particularly in the pharmaceutical industry, medical and public organizations." (Wang Songgui, Zentralblatt MATH, Vol. 956, 2001)Table of ContentsIntroduction * Examples * A model for Longitudinal Data * Exploratory Data Analysis * Estimation of the Marginal Model * Inference for the Marginal Model * Inference for the Random Effects * Fitting Linear Mixed Models with SAS * General Guidelines for Model Building * Exploring Serial Correlation * Local Influence for the Linear Mixed Model * The Heterogeneity Model * Conditional Linear Mixed Models * Exploring Incomplete Data * Joint Modeling of Measurements and Missingness * Simple Missing Data Methods * Selection Models * Pattern-Mixture Models * Sensitivity Analysis for Selection Models * Sensitivity Analysis for Models * How Ignorable is Missing at Random? * The Expectation-Maximization Algorithm * Design Considerations * Case Studies

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Book SynopsisIntended as a companion for textbooks in mathematical methods for science and engineering, this book presents a large number of numerical topics and exercises together with discussions of methods for solving such problems using Mathematica(R).Trade ReviewFrom the reviews:"From a stylistic perspective the book strikes a comfortable balance between explanation and example which makes it easy to dip into and attractive to work through. For the eager reader there is always the promise of an interesting result after half an hour of labour. … The target audience of this book is likely to be a Physics undergraduate finishing his or her first year of study." (Dr. E. J. Grace, Contemporary Physics, Vol. 45 (2), 2004)"Initially this book has been designed as a companion to the undergraduate textbook ‘Mathematical methods’ … and later on developed into a self-contained introduction to the use of computer algebra system (CAS) Mathematica tailored specifically for undergraduate students in physics and related fields. … The book is written in a transparent manner and does not require any prior knowledge of physics for mastering computational techniques. … thanks to a massive array of carefully selected and nicely explained examples from undergraduate physics." (Yuri V. Rogovchenko, Zentralblatt MATH, Vol. 1028, 2004)"This book is intended to be a companion for textbooks in mathematical methods for undergraduate science and engineering students. It presents a number of numerical topics and exercises together with discussions of methods needed for solving problems with Mathematica. … In conclusion, this very well produced and illustrated book is heartily recommended … ." (André Hautot, Gary J. Long, Physicalia, Vol. 26 (1), 2004)Table of ContentsMathematica in a Nutshell / Vectors and Matrices in Mathematica / Integration / Infinite Series and Finite Sums / Numerical Solutions of ODE's: Theory / Numerical Solutions of ODE's: Examples Using Mathematica

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Book SynopsisThis volume deals with controllability and observability properties of nonlinear systems, as well as various ways to obtain input-output representations. The emphasis is on fundamental notions as (controlled) invariant distributions and submanifolds, together with algorithms to compute the required feedbacks. Table of Contents1 Introduction.- 2 Manifolds, Vectorfields, Lie Brackets, Distributions.- 3 Controllability and Observability, Local Decompositions.- 4 Input-Output Representations.- 5 State Space Transformation and Feedback.- 6 Feedback Linearization of Nonlinear Systems.- 7 Controlled Invariant Distribution and the Disturbance Decoupling Problem.- 8 The Input-Output Decoupling Problem.- 9 The Input-Output Decoupling Problem.- 10 Local Stability and Stabilization of Nonlinear Systems.- 11 Controlled Invariant Submanifolds and Nonlinear Zero Dynamics.- 12 Mechanical Nonlinear Control Systems.- 13 Controlled Invariance and Decoupling for General Nonlinear Systems.- 14 Discrete-Time Nonlinear Control Systems.

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Book SynopsisThis book is the result of our teaching over the years an undergraduate course on Linear Optimal Systems to applied mathematicians and a first-year graduate course on Linear Systems to engineers.Table of Contents1 Introduction.- 1.1 Science and Engineering.- 1.2 Physical Systems, Models, and Representations.- 1.3 Robustness.- 2 The System RepresentationR(•) = [A(•),B(•),C(•),D(•)].- 2.1 Fundamental Properties ofR(•).- 2.1.1 Definitions.- 2.1.2 Structure ofR(•).- 2.1.3 State Transition Matrix.- 2.1.4 State Transition Map and Response Map.- 2.1.5 Impulse Response Matrix.- 2.1.6 Adjoint Equations.- 2.1.7 Linear-Quadratic Optimization.- 2.2 Applications.- 2.2.1 Variational Equation.- 2.2.2 Control Correction Example.- 2.2.3 Optimization Example.- 2.2.4 Periodically Varying Differential Equations.- 2d The Discrete-Time System RepresentationRd(•) = [A(•),B(•),C(•),D(•)].- 2d.1 Fundamental Properties ofRd(•).- 2d.2 Application: Periodically Varying Recursion Equations.- 3 The System RepresentationR= [A,B,C,D], Part I.- 3.1 Preliminaries.- 3.2 General Properties ofR= [A,B,C,D].- 3.2.1 Definition.- 3.2.2 State Transition Matrix.- 3.2.3 The State Transition and Response Map of R.- 3.3 Properties of R when A has a Basis of Eigenvectors.- 3d The Discrete-Time System Representation Rd = [A,B,C,D].- 3d.1 Preliminaries.- 3d.2 General Properties of Rd.- 3d.3 Properties of Rd when A has a Basis of Eigenvectors.- 4 The System Representation R = [A,B,C,D], Part II.- 4.1 Preliminaries.- 4.2 Minimal Polynomial.- 4.3 Decomposition Theorem.- 4.4 The Decomposition of a Linear Map.- 4.5 Jordan Form.- 4.6 Function of a Matrix.- 4.7 Spectral Mapping Theorem.- 4.8 The Linear Map X ? AX+XB.- 5 General System Concepts.- 5.1 Dynamical Systems.- 5.2 Time-Invariant Dynamical Systems.- 5.3 Linear Dynamical Systems.- 5.4 Equivalence.- 6 Sampled Data Systems.- 6.1 Relation BetweenL- and z-Transforms.- 6.2 D/A Converter.- 6.3 A/D Converter.- 6.4 Sampled-Data System.- 6.5 Example.- 7 Stability.- 7.1 I/O Stability.- 7.2 State Related Stability Concepts and Applications.- 7.2.1 Stability of x = A(t)x.- 7.2.2 Bounded Trajectories and Regulation.- 7.2.3 Response to T-Periodic Inputs.- 7.2.4 Periodically Varying System with Periodic Input.- 7.2.5 Slightly Nonlinear Systems.- 7d Stability: The Discrete-Time Case.- 7d.1 I/O Stability.- 7d.2 State Related Stability Concepts.- 7d.2.1 Stability of x(k+1) = A(k)x(k).- 7d.2.2 Bounded Trajectories and Regulation.- 7d.2.3 Response to q-Periodic Inputs.- 8 Controllability and Observability.- 8.1 Controllability and Observability of Dynamical Systems.- 8.2 Controllability of the Pair (A(•),B(•)).- 8.2.1 Controllability of the Pair (A(•),B(•)).- 8.2.2 The Cost of Control.- 8.2.3 Stabilization by Linear State Feedback.- 8.3 Observability of the Pair (C(•),A(•)).- 8.4 Duality.- 8.5 Linear Time-Invariant Systems.- 8.5.1 Observability Properties of the Pair (C,A).- 8.5.2 Controllability of the Pair (A,B).- 8.6 Kalman Decomposition Theorem.- 8.7 Hidden Modes, Stabilizability, and Detectability.- 8.8 Balanced Representations.- 8.9 Robustness of Controllability.- 8d Controllability and Observability: The Discrete-Time Case.- 8d.1 Controllability and Observability of Dynamical Systems.- 8d.2 Reachability and Controllability of the Pair (A(•),B(•)).- 8d.2.1 Controllability of the Pair (A(•),B(•)).- 8d.2.2 The Cost of Control.- 8d.3 Observability of the Pair (C(•),A(•)).- 8d.4 Duality.- 8d.5 Linear Time-Invariant Systems.- 8d.5.1 Observability of the Pair (C,A).- 8d.5.2 Reachability and Controllability of the Pair(A,B).- 8d.6 Kalman Decomposition Theorem.- 8d.7 Stabilizability and Detectability.- 9 Realization Theory.- 9.1 Minimal Realizations.- 9.2 Controllable Canonical Form.- 10 Linear State Feedback and Estimation.- 10.1 Linear State Feedback.- 10.2 Linear Output Injection and State Estimation.- 10.3 State Feedback of the Estimated State.- 10.4 Infinite Horizon Linear Quadratic Optimization.- 10d.4 Infinite Horizon Linear Quadratic Optimization. The Discrete-Time Case.- 11 Unity Feedback Systems.- 11.1 The Feedback System ?c.- 11.1.1 State Space Analysis.- 11.1.2 Special Case:R1andR2have no Unstable Hidden Modes.- 11.1.3 The Discrete-Time Case.- 11.2 Nyquist Criterion.- 11.2.1 The Nyquist Criterion.- 11.2.2 Remarks on the Nyquist Criterion.- 11.2.3 Proof of Nyquist Criterion.- 11.2.4 The Discrete-Time Case.- 11.3 Robustness.- 11.3.1 Robustness With Respect to Plant Perturbations.- 11.3.2 Robustness With Respect to Exogenous Disturbances.- 11.3.3 Robust Regulation.- 11.3.4 Bandwidth-Robustness Tradeoff.- 11.3.5 The Discrete-Time Case.- 11.4 Kharitonov’s Theorem.- 11.4.1 Hurwitz Polynomials.- 11.4.2 Kharitonov’s Theorem.- 11.5 Robust Stability Under Structured Perturbations.- 11.5.1 General Robustness Theorem.- 11.5.2 Special Case: Affine Maps and Convexity.- 11.5.3 The Discrete Time Case.- 11.6 Stability Under Arbitrary Additive Plant Perturbations.- 11.7 Transmission Zeros.- 11.7.1 Single-Input Single-Output Case.- 11.7.2 Multi-Input Multi-Output Case: Assumptions and Definitions.- 11.7.3 Characterization of the Zeros.- 11.7.4 Application to Unity Feedback Systems.- Appendix A Linear Maps and Matrix Analysis.- A.1 Preliminary Notions.- A.2 Rings and Fields.- A.3 Linear Spaces.- A4. Linear Maps.- AS. Matrix Representation.- A.5.1 The Concept of Matrix Representation.- A.5.2 Matrix Representation and Change of Basis.- A.5.3 Range and Null Space: Rank and Nullity.- A.5.4 Echelon Forms of a Matrix.- A.6 Notmed Linear Spaces.- A.6.1 Norms.- A.6.2 Convergence.- A.6.3 Equivalent Norms.- A.6.4 The Lebesgue Spaces 1P and LP [Tay.1].- A.6.5 Continuous Linear Transformations.- A.7 The Adjoint of a Linear Map.- A.7.1 Inner Products.- A.7.2 Adjoints of Continuous Linear Maps.- A.7.3 Properties of the Adjoint.- A.7.4 The Finite Rank Operator Fundamental Lemma.- A.7.5 Singular Value Decomposition (SVD).- Appendix B Differential Equations.- BA Existence and Uniqueness of Solutions.- B.1.1 Assumptions.- B.1.2 Fundamental Theorem.- B.1.3 Construction of a Solution by Iteration.- B.1.4 The Bellman-Gronwall Inequality.- B.1.5 Uniqueness.- B.2 Initial Conditions and Parameter Perturbations.- B.3 Geometric Interpretation and Numerical Calculations.- Appendix C Laplace Transforms.- C.1 Definition of the Laplace Transform.- C.2 Properties of Laplace Transforms.- Appendix D the z-Transform.- D.1 Definition of the z-Transform.- D.2 Properties of the z-Transform.- References.- Abbreviations.- Mathematical Symbols.

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Book SynopsisI Fundamentals.- 1 Ordinary Differential Equations.- 2 Difference Equations.- II Local Analysis.- 3 Approximate Solution of Linear Differential Equations.- 4 Approximate Solution of Nonlinear Differential Equations.- 5 Approximate Solution of Difference Equations.- 6 Asymptotic Expansion of Integrals.- III Perturbation Methods.- 7 Perturbation Series.- 8 Summation of Series.- IV Global Analysis.- 9 Boundary Layer Theory.- 10 WKB Theory.- 11 Multiple-Scale Analysis.Trade Review"This book is a reprint of the original published by McGraw-Hill \ref [MR0538168 (80d:00030)]. The only changes are the addition of the Roman numeral I to the title and the provision of a subtitle, "Asymptotic methods and perturbation theory". This latter improvement is much needed, as the original title suggested that this was a teaching book for undergraduate scientists and engineers. It is not, but is an excellent introduction to asymptotic and perturbation methods for master's degree students or beginning research students. Certain parts of it could be used for a course in asymptotics for final year undergraduates in applied mathematics or mathematical physics. This is a book that has stood the test of time and I cannot but endorse the remarks of the original reviewer. It is written in a fresh and lively style and the many graphs and tables, comparing the results of exact and approximate methods, were in advance of its time. I have owned a copy of the original for over twenty years, using it on a regular basis, and, after the original had gone out of print, lending it to my research students. Springer-Verlag has done a great service to users of, and researchers in, asymptotics and perturbation theory by reprinting this classic." (A.D. Wood, Mathematical Reviews) Table of ContentsI Preface. 1 Ordinary Differential Equations. 2 Difference Equations. 3 Approximate Solution of Linear Differential Equations. 4 Approximate Solution of Nonlinear Equations. 5 Approximate Solution of Difference Equations. 6 Asymptotic Expansion of Integrals. 7 Perturbation Series. 8 Summation of Series. 9 Boundary Layer Theory. 10 WKB Theory. 11 Multiple Scales Analysis. Appendix, References, Index

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Book SynopsisBy including classical results as well as recent developments in the field of hydrodynamic stability and transition, the book can be used as a textbook for an introductory, graduate-level course in stability theory or for a special-topics fluids course.Trade ReviewFrom the reviews: SIAM REVIEW "This book presents a modern treatment of stability in shear flows. Stability theory has seen a number of classic treatments over the years…Schmid and Henningson’s book builds on these and offers much new material relevant to stability in shear flows…The MATLAB codes included in the appendix and a discussion of the effects of rounding error and resolution on the computations of eigenvalues of linear stability operators will be particularly helpful for students and researchers as they get started with stability computations…As the basis for a course, the first part of the book would permit students to build a solid foundation in classical and modern stability theory, while a selection of advanced topics from the second half of the book could be treated later in the course or through projects and independent study by students." ZENTRALBLATT MATH "The book addresses to graduate students as well as to a broad community of researchers with a basic knowledge of fundamental fluid dynamics…The topics are treated with mathematical rigor while the physical motivation and usefulness of mathematical concepts is kept close at hand. The work is elegantly structured, and the graphical material is very suggestive."Table of Contents1 Introduction and General Results.- 1.1 Introduction.- 1.2 Nonlinear Disturbance Equations.- 1.3 Definition of Stability and Critical Reynolds Numbers.- 1.3.1 Definition of Stability.- 1.3.2 Critical Reynolds Numbers.- 1.3.3 Spatial Evolution of Disturbances.- 1.4 The Reynolds-Orr Equation.- 1.4.1 Derivation of the Reynolds-Orr Equation.- 1.4.2 The Need for Linear Growth Mechanisms.- I Temporal Stability of Parallel Shear Flows.- 2 Linear Inviscid Analysis.- 2.1 Inviscid Linear Stability Equations.- 2.2 Modal Solutions.- 2.2.1 General Results.- 2.2.2 Dispersive Effects and Wave Packets.- 2.3 Initial Value Problem.- 2.3.1 The Inviscid Initial Value Problem.- 2.3.2 Laplace Transform Solution.- 2.3.3 Solutions to the Normal Vorticity Equation.- 2.3.4 Example: Couette Flow.- 2.3.5 Localized Disturbances.- 3 Eigensolutions to the Viscous Problem.- 3.1 Viscous Linear Stability Equations.- 3.1.1 The Velocity-Vorticity Formulation.- 3.1.2 The Orr-Sommerfeld and Squire Equations.- 3.1.3 Squire’s Transformation and Squire’s Theorem.- 3.1.4 Vector Modes.- 3.1.5 Pipe Flow.- 3.2 Spectra and Eigenfunctions.- 3.2.1 Discrete Spectrum.- 3.2.2 Neutral Curves.- 3.2.3 Continuous Spectrum.- 3.2.4 Asymptotic Results.- 3.3 Further Results on Spectra and Eigenfunctions.- 3.3.1 Adjoint Problem and Bi-Orthogonality Condition.- 3.3.2 Sensitivity of Eigenvalues.- 3.3.3 Pseudo-Eigenvalues.- 3.3.4 Bounds on Eigenvalues.- 3.3.5 Dispersive Effects and Wave Packets.- 4 The Viscous Initial Value Problem.- 4.1 The Viscous Initial Value Problem.- 4.1.1 Motivation.- 4.1.2 Derivation of the Disturbance Equations.- 4.1.3 Disturbance Measure.- 4.2 The Forced Squire Equation and Transient Growth.- 4.2.1 Eigenfunction Expansion.- 4.2.2 Blasius Boundary Layer Flow.- 4.3 The Complete Solution to the Initial Value Problem.- 4.3.1 Continuous Formulation.- 4.3.2 Discrete Formulation.- 4.4 Optimal Growth.- 4.4.1 The Matrix Exponential.- 4.4.2 Maximum Amplification.- 4.4.3 Optimal Disturbances.- 4.4.4 Reynolds Number Dependence of Optimal Growth.- 4.5 Optimal Response and Optimal Growth Rate.- 4.5.1 The Forced Problem and the Resolvent.- 4.5.2 Maximum Growth Rate.- 4.5.3 Response to Stochastic Excitation.- 4.6 Estimates of Growth.- 4.6.1 Bounds on Matrix Exponential.- 4.6.2 Conditions for No Growth.- 4.7 Localized Disturbances.- 4.7.1 Choice of Initial Disturbances.- 4.7.2 Examples.- 4.7.3 Asymptotic Behavior.- 5 Nonlinear Stability.- 5.1 Motivation.- 5.1.1 Introduction.- 5.1.2 A Model Problem.- 5.2 Nonlinear Initial Value Problem.- 5.2.1 The Velocity-Vorticity Equations.- 5.3 Weakly Nonlinear Expansion.- 5.3.1 Multiple-Scale Analysis.- 5.3.2 The Landau Equation.- 5.4 Three-Wave Interactions.- 5.4.1 Resonance Conditions.- 5.4.2 Derivation of a Dynamical System.- 5.4.3 Triad Interactions.- 5.5 Solutions to the Nonlinear Initial Value Problem.- 5.5.1 Formal Solutions to the Nonlinear Initial Value Problem.- 5.5.2 Weakly Nonlinear Solutions and the Center Manifold.- 5.5.3 Nonlinear Equilibrium States.- 5.5.4 Numerical Solutions for Localized Disturbances.- 5.6 Energy Theory.- 5.6.1 The Energy Stability Problem.- 5.6.2 Additional Constraints.- II Stability of Complex Flows and Transition.- 6 Temporal Stability of Complex Flows.- 6.1 Effect of Pressure Gradient and Crossflow.- 6.1.1 Falkner-Skan (FS) Boundary Layers.- 6.1.2 Falkner-Skan-Cooke (FSC) Boundary layers.- 6.2 Effect of Rotation and Curvature.- 6.2.1 Curved Channel Flow.- 6.2.2 Rotating Channel Flow.- 6.2.3 Combined Effect of Curvature and Rotation.- 6.3 Effect of Surface Tension.- 6.3.1 Water Table Flow.- 6.3.2 Energy and the Choice of Norm.- 6.3.3 Results.- 6.4 Stability of Unsteady Flow.- 6.4.1 Oscillatory Flow.- 6.4.2 Arbitrary Time Dependence.- 6.5 Effect of Compressibility.- 6.5.1 The Compressible Initial Value Problem.- 6.5.2 Inviscid Instabilities and Rayleigh’s Criterion.- 6.5.3 Viscous Instability.- 6.5.4 Nonmodal Growth.- 7 Growth of Disturbances in Space.- 7.1 Spatial Eigenvalue Analysis.- 7.1.1 Introduction.- 7.1.2 Spatial Spectra.- 7.1.3 Gaster’s Transformation.- 7.1.4 Harmonic Point Source.- 7.2 Absolute Instability.- 7.2.1 The Concept of Absolute Instability.- 7.2.2 Briggs’ Method.- 7.2.3 The Cusp Map.- 7.2.4 Stability of a Two-Dimensional Wake.- 7.2.5 Stability of Rotating Disk Flow.- 7.3 Spatial Initial Value Problem.- 7.3.1 Primitive Variable Formulation.- 7.3.2 Solution of the Spatial Initial Value Problem.- 7.3.3 The Vibrating Ribbon Problem.- 7.4 Nonparallel Effects.- 7.4.1 Asymptotic Methods.- 7.4.2 Parabolic Equations for Steady Disturbances.- 7.4.3 Parabolized Stability Equations (PSE).- 7.4.4 Spatial Optimal Disturbances.- 7.4.5 Global Instability.- 7.5 Nonlinear Effects.- 7.5.1 Nonlinear Wave Interactions.- 7.5.2 Nonlinear Parabolized Stability Equations.- 7.5.3 Examples.- 7.6 Disturbance Environment and Receptivity.- 7.6.1 Introduction.- 7.6.2 Nonlocalized and Localized Receptivity.- 7.6.3 An Adjoint Approach to Receptivity.- 7.6.4 Receptivity Using Parabolic Evolution Equations.- 8 Secondary Instability.- 8.1 Introduction.- 8.2 Secondary Instability of Two-Dimensional Waves.- 8.2.1 Derivation of the Equations.- 8.2.2 Numerical Results.- 8.2.3 Elliptical Instability.- 8.3 Secondary Instability of Vortices and Streaks.- 8.3.1 Governing Equations.- 8.3.2 Examples of Secondary Instability of Streaks and Vortices.- 8.4 Eckhaus Instability.- 8.4.1 Secondary Instability of Parallel Flows.- 8.4.2 Parabolic Equations for Spatial Eckhaus Instability.- 9 Transition to Turbulence.- 9.1 Transition Scenarios and Thresholds.- 9.1.1 Introduction.- 9.1.2 Three Transition Scenarios.- 9.1.3 The Most Likely Transition Scenario.- 9.1.4 Conclusions.- 9.2 Breakdown of Two-Dimensional Waves.- 9.2.1 The Zero Pressure Gradient Boundary Layer.- 9.2.2 Breakdown of Mixing Layers.- 9.3 Streak Breakdown.- 9.3.1 Streaks Forced by Blowing or Suction.- 9.3.2 Freestream Turbulence.- 9.4 Oblique Transition.- 9.4.1 Experiments and Simulations in Blasius Flow.- 9.4.2 Transition in a Separation Bubble.- 9.4.3 Compressible Oblique Transition.- 9.5 Transition of Vortex-Dominated Flows.- 9.5.1 Transition in Flows with Curvature.- 9.5.2 Direct Numerical Simulations of Secondary Instability of Crossflow Vortices.- 9.5.3 Experimental Investigations of Breakdown of Cross-flow Vortices.- 9.6 Breakdown of Localized Disturbances.- 9.6.1 Experimental Results for Boundary Layers.- 9.6.2 Direct Numerical Simulations in Boundary Layers.- 9.7 Transition Modeling.- 9.7.1 Low-Dimensional Models of Subcritical Transition.- 9.7.2 Traditional Transition Prediction Models.- 9.7.3 Transition Prediction Models Based on Nonmodal Growth.- 9.7.4 Nonlinear Transition Modeling.- III Appendix.- A Numerical Issues and Computer Programs.- A.1 Global versus Local Methods.- A.2 Runge-Kutta Methods.- A.3 Chebyshev Expansions.- A.4 Infinite Domain and Continuous Spectrum.- A.5 Chebyshev Discretization of the Orr-Sommerfeld Equation.- A.6 MATLAB Codes for Hydrodynamic Stability Calculations.- A.7 Eigenvalues of Parallel Shear Flows.- B Resonances and Degeneracies.- B.1 Resonances and Degeneracies.- B.2 Orr-Sommerfeld-Squire Resonance.- C Adjoint of the Linearized Boundary Layer Equation.- C.1 Adjoint of the Linearized Boundary Layer Equation.- D Selected Problems on Part I.

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Book Synopsis(iv) The chapter on int~gral theorems, now Chapter 5, has been expanded to include an altemative proof of Gauss's theorem, a treatmeot of Green's theorem and a more extended discussioo of the classification of vector fields.Table of Contents1 Definitions. Addition of Vectors.- 1. Scalar and Vector Quantities.- 2. Graphical Representation of Vectors.- 3. Addition and Subtraction of Vectors.- 4. Components of a Vector.- 5. Geometrical Applications.- 6. Scalar and Vector Fields.- Miscellaneous Exercises I.- 2 Products of Vectors.- 1. General.- 2. The Scalar Product.- 3. The Vector Product.- 4. Vector Area.- 5. Application to Vector Products.- 6. Products of Three Vectors.- 7. Line and Surface Integrals as Scalar Products.- Miscellaneous Exercises II.- 3 The Differentiation of Vectors.- 1. Scalar Differentiation.- 2. Differentiation of Sums and Products.- 3. Partial Differentiation.- Miscellaneous Exercises III.- 4 The Operator ? and Its Uses.- 1. The Operator ?.- 2. The Gradient of a Scalar Field.- 3. The Divergence of a Vector Field.- 4. The Operator div grad..- 5. The Operator ?2 with Vector Operand.- 6. The Curl of a Vector Field.- 7. Simple Examples of the Curl of a Vector Field.- 8. Divergence of a Vector Product.- 9. Divergence and Curl of SA.- 10. The Operator curl grad..- 11. The Operator grad div..- 12. The Operator div curl..- 13. The Operator curl curl..- 14. The Vector Field grad (k/r).- 15. Vector Operators in Terms of Polar Co-ordinates.- Miscellaneous Exercises IV.- 5 Integral Theorems.- 1. The Divergence Theorem of Gauss.- 2. Gauss’s Theorem and the Inverse Square Law.- 3. Green’s Theorem.- 4. Stokes’s Theorem.- 5. Alternative Definitions of Divergence and Curl.- 6. Classification of Vector Fields.- Miscellaneous Exercises V.- 6 The Scalar Potential Field.- 1. General Properties.- 2. The Inverse Square Law. Point Sources.- 3. Volume Distributions.- 4. Multi-valued Potentials.- 7 The Vector Potential Field.- 1. The Magnetic Field of a Steady Current.- 2. The Vector Potential.- 3. Linear Currents.- 4. Simple Examples of Vector Potential.- 8 The Electromagnetic Field Equations of Maxwell.- 1. General.- 2. Maxwell’s Equations.- 3. Energy Considerations.- Miscellaneous Exercises VIII.- Answers to Exercises.

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Book SynopsisTable of ContentsPreface Chapter 1 Pavement Performance Data Chapter 2 Fundamentals of statistics Chapter 3 Design of experiments Chapter 4 Regression Chapter 5 Logistic regression Chapter 6 Count data models Chapter 7 Survival analysis Chapter 8 Time series Chapter 9 Stochastic process Chapter 10 Decision trees and ensemble learning Chapter 11 Neural networks Chapter 12 Support vector machine and k-nearest neighbors Chapter 13 Principal component analysis Chapter 14 Factor analysis Chapter 15 Cluster analysis Chapter 16 Discriminant analysis Chapter 17 Structural equation model Chapter 18 Markov chain Monte Carlo

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