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Books in Engineering mathematics

  • Higher-Order Theories of Functionally Graded Circular Cylindrical Beams and Columns

    • 1st Edition
    • Xueyang Zhang + 3 more
    • English
    Higher-Order Theories of Functionally Graded Circular Cylindrical Beams and Columns provides a detailed exploration of the mechanical behaviors of functionally graded circular cylindrical beams and columns, including bending, vibration, and stability, and considering factors such as shear deformation, cross-section warping, elastic foundation interaction, non-local effects in nanoscale and piezoelectric and flexoelectric, and more. With a focus on higher-order theories and advanced analytical methods, the book enables researchers to gain a deeper understanding of the fundamental principles governing the behavior of circular cylindrical structures. The detailed derivations and explanations of complex equations and concepts assist in the development of new research.The book helps engineers identify and address potential issues in the design and analysis of circular cylindrical structures. Detailed discussions on factors affecting mechanical performance, such as material selection, geometric parameters, and loading conditions enable engineers to make informed decisions and optimize their designs for improved performance and safety.

  • Foundations of Mathematical Modeling and Analysis in Engineering

    • 1st Edition
    • A. Ted Watson
    • English
    Foundations of Mathematical Modeling and Analysis in Engineering offers a comprehensivepresent... of the mathematical principles underpinning modern engineering and the quantitative sciences, and develops mathematical modeling as a foundational intellectual discipline. Designed for graduate and advanced undergraduate students, and accessible to seasoned professionals, it provides the means to develop and employ mathematical representations of physical processes and systems.

  • Introduction to Digital Communications

    • 2nd Edition
    • Ali Grami
    • English
    Introduction to Digital Communications, written especially for upper-level undergraduate courses, explores the basic principles in the analysis and design of digital communication systems, including design objectives, constraints and trade-offs. After portraying the big picture and laying the background material, this book lucidly progresses to a comprehensive and detailed discussion of all critical elements and key functions in digital communications. The second edition has been fully revised, with timely new chapters on wireless enabling systems and encryption, more practical examples, more application-focused real-world end of chapter exercises, and a more crisp and concise approach to the content.

  • Fundamentals of Uncertainty Quantification for Engineers

    Methods and Models
    • 1st Edition
    • Yan Wang + 2 more
    • English
    Fundamentals of Uncertainty Quantification for Engineers: Methods and Models provides a comprehensive introduction to uncertainty quantification (UQ) accompanied by a wide variety of applied examples and implementation details to reinforce the concepts outlined in the book. Sections start with an introduction to the history of probability theory and an overview of recent developments of UQ methods in the domains of applied mathematics and data science. Major concepts of copula, Monte Carlo sampling, Markov chain Monte Carlo, polynomial regression, Gaussian process regression, polynomial chaos expansion, stochastic collocation, Bayesian inference, modelform uncertainty, multi-fidelity modeling, model validation, local and global sensitivity analyses, linear and nonlinear dimensionality reduction are included. Advanced UQ methods are also introduced, including stochastic processes, stochastic differential equations, random fields, fractional stochastic differential equations, hidden Markov model, linear Gaussian state space model, as well as non-probabilistic methods such as robust Bayesian analysis, Dempster-Shafer theory, imprecise probability, and interval probability. The book also includes example applications in multiscale modeling, reliability, fatigue, materials design, machine learning, and decision making.

  • Integral Manifolds for Impulsive Differential Problems with Applications

    • 1st Edition
    • Ivanka Stamova + 1 more
    • English
    Integral Manifolds for Impulsive Differential Problems with Applications offers readers a comprehensive resource on integral manifolds for different classes of differential equations which will be of prime importance to researchers in applied mathematics, engineering, and physics. The book offers a highly application-oriented approach, reviewing the qualitative properties of integral manifolds which have significant practical applications in emerging areas such as optimal control, biology, mechanics, medicine, biotechnologies, electronics, and economics. For applied scientists, this will be an important introduction to the qualitative theory of impulsive and fractional equations which will be key in their initial steps towards adopting results and methods in their research.

  • Reliability Assessment and Optimization of Complex Systems

    • 1st Edition
    • Akshay Kumar + 2 more
    • English
    Reliability Assessment and Optimization of Complex Systems delves into a range of tools and techniques for designing optimized complex systems. Each chapter explores system modeling and the implementation of various metaheuristics for optimization purposes. This book provides readers in the domain of applied mathematics with a comprehensive understanding of system reliability analysis and improvement, thereby offering substantial value to their knowledge and expertise. System reliability has become the paramount attribute of any production unit. The process of maximizing system reliability while adhering to multiple constraints is referred to as reliability optimization.There are two primary approaches to enhancing a system's performance and reliability: developing a product with reduced failures (failure avoidance) or incorporating resilience to ensure the system continues functioning even in the event of a failure (fault tolerance).

  • Engineering Analysis

    Advanced Mathematical Methods for Engineers
    • 1st Edition
    • Zhihe Jin
    • English
    Engineering Analysis: Advanced Mathematical Methods for Engineers introduces graduate engineering students to the fundamental but advanced mathematics tools used in engineering application, especially in mechanical, aerospace, and civil engineering. Most engineering problems are described by differential equations, particularly partial differential equations (PDEs). Deformation and failure in solid structures, fluid flow, heat transfer, and mass diffusion are all governed by PDEs in general. Many physical quantities in engineering are tensors, including deformation gradient, strain rates, stresses, elastic stiffness, and thermal conductivity of composite materials. This book helps engineering graduate students develop the skills to establish the mathematical models of engineering problems and to solve the problems described by the mathematical models.

  • MATLAB

    A Practical Introduction to Programming and Problem Solving
    • 6th Edition
    • Dorothy C. Attaway
    • English
    MATLAB: A Practical Introduction to Programming and Problem Solving, winner of TAA’s 2017 Textbook Excellence Award ("Texty"), guides the reader through both programming and built-in functions to easily exploit MATLAB’s extensive capabilities for tackling engineering and scientific problems. Assuming no knowledge of programming, this book starts with programming concepts, such as variables, assignments, and selection statements, moves on to loops, and then solves problems using both the programming concept and the power of MATLAB. The sixth edition has been updated to reflect the functionality of the current version of MATLAB (R2021a), including the introduction of machine learning concepts and the Machine Learning Toolbox, and new sections on data formats and data scrubbing.

  • Introduction to Chemical Engineering Analysis Using Mathematica

    for Chemists, Biotechnologists and Materials Scientists
    • 2nd Edition
    • Henry C. Foley
    • English
    Introduction to Chemical Engineering Analysis Using Mathematica, Second Edition reviews the processes and designs used to manufacture, use, and dispose of chemical products using Mathematica, one of the most powerful mathematical software tools available for symbolic, numerical, and graphical computing. Analysis and computation are explained simultaneously. The book covers the core concepts of chemical engineering, ranging from the conservation of mass and energy to chemical kinetics. The text also shows how to use the latest version of Mathematica, from the basics of writing a few lines of code through developing entire analysis programs. This second edition has been fully revised and updated, and includes analyses of the conservation of energy, whereas the first edition focused on the conservation of mass and ordinary differential equations.

  • Handbook of Probabilistic Models

    • 1st Edition
    • Pijush Samui + 3 more
    • English
    Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.