Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction to Python
- 2 Systems of Linear Algebraic Equations
- 3 Interpolation and Curve Fitting
- 4 Roots of Equations
- 5 Numerical Differentiation
- 6 Numerical Integration
- 7 Initial Value Problems
- 8 Two-Point Boundary Value Problems
- 9 Symmetric Matrix Eigenvalue Problems
- 10 Introduction to Optimization
- Appendices
- List of Program Modules (by Chapter)
- Index
Preface
Published online by Cambridge University Press: 05 June 2014
- Frontmatter
- Contents
- Preface
- 1 Introduction to Python
- 2 Systems of Linear Algebraic Equations
- 3 Interpolation and Curve Fitting
- 4 Roots of Equations
- 5 Numerical Differentiation
- 6 Numerical Integration
- 7 Initial Value Problems
- 8 Two-Point Boundary Value Problems
- 9 Symmetric Matrix Eigenvalue Problems
- 10 Introduction to Optimization
- Appendices
- List of Program Modules (by Chapter)
- Index
Summary
This book is targeted toward engineers and engineering students of advanced standing (juniors, seniors, and graduate students). Familiarity with a computer language is required; knowledge of engineering mechanics (statics, dynamics, and mechanics of materials) is useful, but not essential.
The primary purpose of the text is to teach numerical methods. It is not a primer on Python programming. We introduce just enough Python to implement the numerical algorithms. That leaves the vast majority of the language unexplored.
Most engineers are not programmers, but problem solvers. They want to know what methods can be applied to a given problem, what their strengths and pitfalls are, and how to implement them. Engineers are not expected to write computer code for basic tasks from scratch; they are more likely to use functions and subroutines that have been already written and tested. Thus, programming by engineers is largely confined to assembling existing bits of code into a coherent package that solves the problem at hand.
The “bit” of code is usually a function that implements a specific task. For the user the details of the code are unimportant. What matters are the interface (what goes in and what comes out) and an understanding of the method on which the algorithm is based. Because no numerical algorithm is infallible, the importance of understanding the underlying method cannot be overemphasized; it is, in fact, the rationale behind learning numerical methods.
- Type
- Chapter
- Information
- Numerical Methods in Engineering with Python 3 , pp. ix - xiiPublisher: Cambridge University PressPrint publication year: 2013