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Books in Engineering mathematics

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    • Foundations of Mathematical Modeling and Analysis in Engineering

      • 1st Edition
      • December 11, 2025
      • A. Ted Watson
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 5 9 2 8
      • eBook
        9 7 8 0 4 4 3 2 9 5 9 3 5
      Foundations of Mathematical Modeling and Analysis for Engineering is designed for first-year graduate and advanced undergraduate engineering students. The book explores linear system theory and demonstrates its application in developing analytical solutions to various equations, essential for describing physical systems through mathematical modeling. This foundation is crucial for learning and research in engineering and various scientific fields. It equips students with the mathematical tools needed to solve entire classes of linear algebraic, ordinary-, and partial-differential equations, while also teaching principles for formulating, organizing, and solving linear subsystems, all of which are vital components of both linear and nonlinear mathematical models. This knowledge prepares students for advanced studies in engineering, applied mathematics, and foundational sciences.

    • Fundamentals of Uncertainty Quantification for Engineers

      • 1st Edition
      • May 5, 2025
      • Yan Wang + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 3 6 6 1 0
      • eBook
        9 7 8 0 4 4 3 1 3 6 6 2 7
      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
      • April 24, 2025
      • Ivanka Stamova + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 0 1 3 4 6
      • eBook
        9 7 8 0 4 4 3 3 0 1 3 5 3
      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.

    • Introduction to Digital Communications

      • 2nd Edition
      • August 7, 2025
      • Ali Grami
      • English
      • Paperback
        9 7 8 0 4 4 3 2 3 7 9 5 9
      • eBook
        9 7 8 0 4 4 3 2 3 7 9 4 2
      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.

    • Engineering Analysis

      • 1st Edition
      • May 20, 2024
      • Zhihe Jin
      • English
      • Paperback
        9 7 8 0 3 2 3 9 5 3 9 7 9
      • eBook
        9 7 8 0 3 2 3 9 5 3 9 6 2
      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.

    • Reliability Assessment and Optimization of Complex Systems

      • 1st Edition
      • October 31, 2024
      • Akshay Kumar + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 1 1 2 8
      • eBook
        9 7 8 0 4 4 3 2 9 1 1 3 5
      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).

    • MATLAB

      • 6th Edition
      • May 4, 2022
      • Dorothy C. Attaway
      • English
      • Paperback
        9 7 8 0 3 2 3 9 1 7 5 0 6
      • eBook
        9 7 8 0 3 2 3 9 8 6 1 1 3
      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.

    • Statistics for Biomedical Engineers and Scientists

      • 1st Edition
      • May 18, 2019
      • Andrew P. King + 1 more
      • English
      • Paperback
        9 7 8 0 0 8 1 0 2 9 3 9 8
      • eBook
        9 7 8 0 0 8 1 0 2 9 4 0 4
      Statistics for Biomedical Engineers and Scientists: How to Analyze and Visualize Data provides an intuitive understanding of the concepts of basic statistics, with a focus on solving biomedical problems. Readers will learn how to understand the fundamental concepts of descriptive and inferential statistics, analyze data and choose an appropriate hypothesis test to answer a given question, compute numerical statistical measures and perform hypothesis tests ‘by hand’, and visualize data and perform statistical analysis using MATLAB. Practical activities and exercises are provided, making this an ideal resource for students in biomedical engineering and the biomedical sciences who are in a course on basic statistics.

    • Handbook of Probabilistic Models

      • 1st Edition
      • October 5, 2019
      • Pijush Samui + 3 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 6 5 1 4 0
      • eBook
        9 7 8 0 1 2 8 1 6 5 4 6 1
      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.

    • Hybrid Machining

      • 1st Edition
      • June 19, 2018
      • Xichun Luo + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 3 0 5 9 9
      • eBook
        9 7 8 0 1 2 8 1 3 1 1 3 8
      Hybrid Machining: Theory, Methods, and Case Studies covers the scientific fundamentals, techniques, applications and real-world descriptions of emerging hybrid machining technology. This field is advancing rapidly in industrial and academic contexts, creating a great need for the fundamental and technical guidance that this book provides. The book includes discussions of basic concepts, process design principles, standard hybrid machining processes, multi-scale modeling approaches, design, on-machine metrology and work handling systems. Readers interested in manufacturing systems, product design or machining technology will find this one-stop guide to hybrid machining the ideal reference.