Python Scripting for Computational Science

Texts in Computational Science and Engineering

Book 3
Springer Science & Business Media
2
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Numerous readers of the second edition have noti?ed me about misprints and possible improvements of the text and the associated computer codes. The resulting modi?cations have been incorporated in this new edition and its accompanying software. The major change between the second and third editions, however, is caused by the new implementation of Numerical Python, now called numpy. The new numpy package encourages a slightly di?erent syntax compared to the old Numeric implementation, which was used in the previous editions. Since Numerical Python functionality appears in a lot of places in the book, there are hence a huge number of updates to the new suggested numpy syntax, especially in Chapters 4, 9, and 10. The second edition was based on Python version 2.3, while the third edition contains updates for version 2.5. Recent Python features, such as generator expressions (Chapter 8.9.4), Ctypes for interfacing shared libraries in C (Chapter 5.2.2), the with statement (Chapter 3.1.4), and the subprocess module for running external processes (Chapter 3.1.3) have been exempli?ed to make the reader aware of new tools. Chapter 4.4.4 is new and gives a taste of symbolic mathematics in Python.
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Additional Information

Publisher
Springer Science & Business Media
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Published on
Dec 5, 2007
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Pages
756
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ISBN
9783540739166
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Best For
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Language
English
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Genres
Computers / Computer Science
Computers / General
Computers / Intelligence (AI) & Semantics
Computers / Software Development & Engineering / General
Mathematics / Applied
Science / Physics / Mathematical & Computational
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Content Protection
This content is DRM protected.
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The second edition features lots of improvements and new material. The most significant additions include - finite difference methods and implementations for a 1D time-dependent heat equation (Chapter 1. 7. 6), - a solver for vibration of elastic structures (Chapter 5. 1. 6), - a step-by-step instruction of how to develop and test Diffpack programs for a physical application (Chapters 3. 6 and 3. 13), - construction of non-trivial grids using super elements (Chapters 3. 5. 4, 3. 6. 4, and 3. 13. 4), - additional material on local mesh refinements (Chapter 3. 7), - coupling of Diffpack with other types of software (Appendix B. 3) - high-level programming offinite difference solvers utilizing the new stencil (finite difference operator) concept in Diffpack (Appendix D. 8). Many of the examples, projects, and exercises from the first edition have been revised and improved. Some new exercises and projects have also been added. A hopefully very useful new feature is the compact overview of all the program examples in the book and the associated software files, presented in Chapter 1. 2. Errors have been corrected, many explanations have been extended, and the text has been upgraded to be compatible with Diffpack version 4. 0. The major difficulty when developing programs for numerical solution of partial differential equations is to debug and verify the implementation. This requires an interplay between understanding the mathematical model,the in volved numerics, and the programming tools.
The book serves both as a reference for various scaled models with corresponding dimensionless numbers, and as a resource for learning the art of scaling. A special feature of the book is the emphasis on how to create software for scaled models, based on existing software for unscaled models.

Scaling (or non-dimensionalization) is a mathematical technique that greatly simplifies the setting of input parameters in numerical simulations. Moreover, scaling enhances the understanding of how different physical processes interact in a differential equation model. Compared to the existing literature, where the topic of scaling is frequently encountered, but very often in only a brief and shallow setting, the present book gives much more thorough explanations of how to reason about finding the right scales. This process is highly problem dependent, and therefore the book features a lot of worked examples, from very simple ODEs to systems of PDEs, especially from fluid mechanics.

The text is easily accessible and example-driven. The first part on ODEs fits even a lower undergraduate level, while the most advanced multiphysics fluid mechanics examples target the graduate level. The scientific literature is full of scaled models, but in most of the cases, the scales are just stated without thorough mathematical reasoning. This book explains how the scales are found mathematically.

This book will be a valuable read for anyone doing numerical simulations based on ordinary or partial differential equations.

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