Introduction to Computational Engineering with MATLAB® aims to teach readers how to use MATLAB programming to solve numerical engineering problems. The book focuses on computational engineering with the objective of helping engineering students improve their numerical problem-solving skills. The book cuts a middle path between undergraduate texts that simply focus on programming and advanced mathematical texts that skip over foundational concepts, feature cryptic mathematical expressions, and do not provide sufficient support for novices.
Although this book covers some advanced topics, readers do not need prior computer programming experience or an advanced mathematical background. Instead, the focus is on learning how to leverage the computer and software environment to do the hard work. The problem areas discussed are related to data-driven engineering, statistics, linear algebra, and numerical methods. Some example problems discussed touch on robotics, control systems, and machine learning.
This second edition includes several corrections, additions, and new exercises across all chapters. The linear algebra chapter is reorganized with the least squares regression, eigenvalue, SVD, and PCA material separated into their own chapters. Additional content was also added on the Cholesky decomposition of positive definite matrices, numerical stability, and applications of the orthogonal QR and SVD matrix decompositions in rectangular systems of equations.
A new chapter provides detailed reference material seldom found in undergraduate textbooks, describing modern orthogonal matrix factorization algorithms with source code implementations of QR decomposition, eigendecomposition with the QR and Francis (implicitly shifted QR) algorithms, and the singular value decomposition.
Features
- Demonstrates through algorithms and code segments how numeric problems are solved with only a few lines of MATLAB code
- Quickly teaches students the basics and gets them started programming interesting problems as soon as possible
- No prior computer programming experience or advanced math skills required
- Suitable for students at the undergraduate level who have prior knowledge of college algebra, trigonometry, and are enrolled in Calculus I
- MATLAB script files, functions, and datasets used in examples are available for download from www.routledge.com/9781041169086
Table of Contents:
1. MATLAB Programming 2. Graphical Data Analysis 3. Statistical Data Analysis 4. Using the Symbolic Math Toolbox 5. Introduction to Linear Algebra 6. Least Squares Regression 7. Applications of Eigenvalues and Eigenvectors 8. Singular Value Decomposition (SVD) 9. Principal Component Analysis (PCA) 10. Computational Numerical Methods 11. Orthogonal Matrix Factoring
About the Author :
Tim Bower is a Professor of Robotics and Automation Engineering Technology and Computer Systems Technology at Kansas State University, Salina. He received the B.S. in Electrical Engineering from Kansas State University (K-State) in 1987 and the M.S. in Electrical Engineering from the University of Kansas in 1990. He was a Senior Member of the Technical Staff at Sprint's Local Telephone Division from 1989 to 1998. From 1998 to 2003, he was a systems administration manager and instructor while taking graduate coursework in Computer Science at Kansas State University in Manhattan, Kansas. He joined the faculty at K-State's Salina campus in 2004. He teaches undergraduate courses related to programming in C, Python, and \MATLAB, robotics programming, machine vision, numerical computation, operating systems, data structures and algorithms, and systems administration. He is the recipient of the 2015 and 2016 Excellence in Innovation Award, and the 2025 Marchbanks Memorial Award for Teaching Excellence.
