ISBN-10:
1482250357
ISBN-13:
9781482250350
Pub. Date:
10/05/2015
Publisher:
Taylor & Francis
Numerical Analysis for Engineers: Methods and Applications, Second Edition / Edition 2

Numerical Analysis for Engineers: Methods and Applications, Second Edition / Edition 2

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Overview

Numerical Analysis for Engineers: Methods and Applications demonstrates the power of numerical methods in the context of solving complex engineering and scientific problems. The book helps to prepare future engineers and assists practicing engineers in understanding the fundamentals of numerical methods, especially their applications, limitations, and potentials.

Each chapter contains many computational examples, as well as a section on applications that contain additional engineering examples. Each chapter also includes a set of exercise problems.

The problems are designed to meet the needs of instructors in assigning homework and to help students with practicing the fundamental concepts. Although the book was developed with emphasis on engineering and technological problems, the numerical methods can also be used to solve problems in other fields of science.

Product Details

ISBN-13: 9781482250350
Publisher: Taylor & Francis
Publication date: 10/05/2015
Series: Textbooks in Mathematics Series
Edition description: Revised
Pages: 451
Product dimensions: 7.00(w) x 10.00(h) x 1.10(d)

About the Author

Bilal Ayyub, PhD, is a professor of civil and environmental engineering at the University of Maryland, College Park, and the director of the Center for Technology and Systems Management at the A. James Clark School of Engineering. Dr. Ayyub is a fellow of the American Society of Civil Engineers, the American Society of Mechanical Engineers, the Society of Naval Architects and Marine Engineers, and the Society for Risk Analysis, and is also a senior member of the Institute of Electrical and Electronics Engineers (IEEE). He has completed many research and development projects for many governmental and private entities. Dr. Ayyub has received numerous awards and is the author or coauthor of more than 600 publications in journals, conference proceedings, and reports including 8 textbooks and 14 edited books.

Richard H. McCuen, PhD, is the Ben Dyer Professor of civil and environmental engineering at the University of Maryland, College Park. Dr. McCuen earned degrees from Carnegie Mellon University and the Georgia Institute of Technology. Topics in statistical hydrology and stormwater management are his primary research interests. He received numerous awards and is the author of 26 books and over 250 professional papers.

Table of Contents

Introduction
Numerical Analysis in Engineering
Analytical versus Numerical Analysis
Taylor Series Expansion
Applications
Problems

Matrices
Introduction
Vectors
Determinants
Rank of a Matrix
Applications
Problems

Introduction to Numerical Methods
Introduction
Accuracy, Precision, and Bias
Significant Figures
Analysis of Numerical Errors
Advantages and Disadvantages of Numerical Methods
Applications
Problems

Roots of Equations
Introduction
Eigenvalue Analysis
Direct-Search Method
Bisection Method
Newton–Raphson Iteration
Secant Method
Polynomial Reduction
Synthetic Division
Multiple Roots
Systems of Nonlinear Equations
Applications
Problems

Simultaneous Linear Equations
Introduction
Gaussian Elimination
Gauss–Jordan Elimination
Additional Considerations for Elimination Procedures
LU Decomposition
Iterative Equation-Solving Methods
Use of Determinants
Matrix Inversion
Applications
Problems

Numerical Interpolation
Introduction
Method of Undetermined Coefficients
Gregory–Newton Interpolation Method
Finite-Difference Interpolation
Newton’s Method
Lagrange Polynomials
Interpolation Using Splines
Guidelines for Choice of Interpolation Method
Multidimensional Interpolation
Applications
Problems

Differentiation and Integration
Numerical Differentiation
Numerical Integration
Applications
Problems

Differential Equations
Introduction
Taylor Series Expansion
Euler’s Method
Modified Euler’s Method
Runge–Kutta Methods
Predictor–Corrector Methods
Least-Squares Method
Galerkin Method
Higher Order Differential Equations
Boundary-Value Problems
Integral Equations
Applications
Problems

Data Description and Treatment
Introduction
Classification of Data
Graphical Description of Data
Histograms and Frequency Diagrams
Descriptive Measures
Applications
Problems

Curve Fitting and Regression Analysis
Introduction
Correlation Analysis
Introduction to Regression
Principle of Least Squares
Reliability of the Regression Equation
Correlation versus Regression
Applications of Bivariate Regression Analysis
Multiple Regression Analysis
Regression Analysis of Nonlinear Models
Applications
Problems

Numerical Optimization
Introduction
The Response Surface Analysis
Numerical Least Squares
Steepest Descent Method
Illustrating Applications
Applications
Concluding Remarks
Problems

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