Numerical Methods in Engineering with Python is a text for engineering students and a reference for practicing engineers, especially those who wish to explore the power and efficiency of Python. Examples and applications were chosen for their relevance to real world problems, and where numerical solutions are most efficient. Numerical methods are discussed thoroughly and illustrated with problems involving both hand computation and programming. Computer code accompanies each method and is available on the book web site. This code is made simple and easy to understand by avoiding complex bookkeeping schemes, while maintaining the essential features of the method. Python was chosen as the example language because it is elegant, easy to learn and debug, and its facilities for handling arrays are unsurpassed. Moreover, it is an open-source software package; free and available to all students and engineers. Explore numerical methods with Python, a great language for teaching scientific computation.
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Book Description
Numerical Methods in Engineering with Python is a text for engineering students and a reference for practicing engineers. The numerous examples and applications were chosen for their relevance to real world problems, and where numerical solutions are most efficient. The Python code is available on the book web site.
About the Author
Jaan Kiusalaas is a Professor Emeritus in the School of Engineering Science and Mechanics at Pennsylvania State University. He has taught numerical methods, including finite element and boundary element methods for over 30 years. He is also the co-author of four other works, Engineering Mechanics: Statics, Engineering Mechanics: Dynamics, and Mechanics of Materials and an alternate version of this work with MATLAB code.
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Most Helpful Customer Reviews
20 of 21 people found the following review helpful:
4.0 out of 5 stars
Undergrad Numerical Method Textbook,
By Keiichi Ito (Japan) - See all my reviews
This review is from: Numerical Methods in Engineering with Python (Hardcover)
The content is typical of an undergraduate course in numerical methods. The author employs Python for that. It doesn't tell you how to use various modules available on the net. Instead, the author sticks to the standard few and explains numerical methods from the very basics. The book is very good for an introduction coursework. However, if you are a researcher looking for tools for serious numerical computation and thinking of using Python as an efficient and convenient organizer(or "glue") rather than a number cruncher, you might as well consult the book by H.P. Langtangen.
This book is also useful if you want to get a hang on how a textbook numerical method would work on Python. You can then use the language for a quick algorithm prototyping in your own work.
2 of 2 people found the following review helpful:
4.0 out of 5 stars
Good Introduction,
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This review is from: Numerical Methods in Engineering with Python (Hardcover)
Being familiar with python is necessary for this volume, but the concepts used are simple. The description of the numerical methods is complete, but a comparison of methods is not always present leaving an inexperienced analyst to wonder which method is the best for a given situation. Python is also not the most efficient language for numerical computation, but is a good linker and with cython it can become a viable option. Overall I recommend this book for learning numerical methods as you will not be tangled in the programming language, but learning numerical methods.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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nodal abscissas, recursive trapezoidal rule, import sqrt, first central difference approximation, import array, import dot, import zeros, incremental search method, augmented coefficient matrix, inverse power method, scaled row, composite trapezoidal rule, math module, midpoint method, lower triangular portion, quadrilateral region, upper triangular portion, natural cubic spline, most recent call, pivot row, merit function, cardinal functions, truncation error, tridiagonal form Key Phrases - Capitalized Phrases (CAPs): (learn more)
Solve Prob, Systems of Linear Algebraic Equations, Cambridge University Press, Eigenvalues of Symmetric Tridiagonal Matrices, Numerical Recipes, Solution First, Solution Letting, Solve Example, Use Doolittle, Use the Jacobi New!
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nodal abscissas, recursive trapezoidal rule, import sqrt, first central difference approximation, import array, import dot, import zeros, incremental search method, augmented coefficient matrix, inverse power method, scaled row, composite trapezoidal rule, math module, midpoint method, lower triangular portion, quadrilateral region, upper triangular portion, natural cubic spline, most recent call, pivot row, merit function, cardinal functions, truncation error, tridiagonal form Key Phrases - Capitalized Phrases (CAPs): (learn more)
Solve Prob, Systems of Linear Algebraic Equations, Cambridge University Press, Eigenvalues of Symmetric Tridiagonal Matrices, Numerical Recipes, Solution First, Solution Letting, Solve Example, Use Doolittle, Use the Jacobi New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 11
books:
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- Numerical Recipes: The Art of Scientific Computing by William H. Press on 4 pages
- Numerical Recipes in Fortran: The Art of Scientific Computing by William H. Press on page 111, page 114, and page 219
- Theory of Elasticity (McGraw-Hill Classic Textbook Reissue Series) by S. Timoshenko on page 133
- Principles of Heat Transfer by Frank Kreith on page 14
- Python Essential Reference (3rd Edition) by David M. Beazley on page 3
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