Numerical Methods in Engineering with Python
2nd Edition
Published on 26. April 2010
978-0-511-68592-7 (ISBN)
Description
This text is for engineering students and a reference for practising engineers, especially those who wish to explore Python. This new edition features 18 additional exercises and the addition of rational function interpolation. Brent's method of root finding was replaced by Ridder's method, and the Fletcher-Reeves method of optimization was dropped in favor of the downhill simplex method. Each numerical method is explained in detail, and its shortcomings are pointed out. The examples that follow individual topics fall into two categories: hand computations that illustrate the inner workings of the method and small programs that show how the computer code is utilized in solving a problem. This second edition also includes more robust computer code with each method, which is available on the book website. This code is made simple and easy to understand by avoiding complex bookkeeping schemes, while maintaining the essential features of the method.
More details
Edition
2nd Revised edition
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
College/higher education
Professional and scholarly
Edition type
Revised edition
Illustrations
Worked examples or Exercises; 20 Tables, unspecified
File size
4,37 MB
ISBN-13
978-0-511-68592-7 (9780511685927)
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Schweitzer Classification
Other editions
Additional editions
Jaan Kiusalaas
Numerical Methods in Engineering with Python
Book
01/2010
2nd Edition
Cambridge University Press
€76.75
Article exhausted; check for reprint
Person
Jaan Kiusalaas is a Professor Emeritus in the Department of Engineering Science and Mechanics at Pennsylvania State University. He has taught computer methods, including finite element and boundary element methods, for more than 30 years. He is also the co-author of four other books - Engineering Mechanics: Statics, Engineering Mechanics: Dynamics, Mechanics of Materials, and an alternate version of this work with MATLAB (R) code.
Content
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.
