LANGTANGEN PYTHON SCRIPTING FOR COMPUTATIONAL SCIENCE PDF

Artificial Intelligence Back cover copy Now in its revised and updated 4th edition, this book teaches computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs written in the easy-to-learn, high-level Python language. As in the previous edition, the focus is on highly relevant examples and applications. These include binding together existing applications and tools, for example, for automating simulation, data analysis, and visualization. The highly regarded author, Hans Petter Langtangen, demonstrates that scripting with Python helps the programmer achieve much greater productivity, increases the reliability of scientific work and lets programmers have more fun - on Unix, Windows and Macintosh. The fourth edition corrects, updates and improves the implementation of a range of tools that have seen significant changes in recent years. The open source software tools and examples associated with the book are also improved.

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There are many books on the use of numerical methods for solving engineering problems and for modeling of engineering artifacts. In addition there are many styles of such presentations ranging from books with a major emphasis on theory to books with an emphasis on applications.

The purpose of this book is hopefully to present a somewhat different approach to the use of numerical methods for - gineering applications. Engineering models are in general nonlinear models where the response of some appropriate engineering variable depends in a nonlinear manner on the - plication of some independent parameter. It is certainly true that for many types of engineering models it is sufficient to approximate the real physical world by some linear model.

However, when engineering environments are pushed to - treme conditions, nonlinear effects are always encountered. It is also such - treme conditions that are of major importance in determining the reliability or failure limits of engineering systems. Hence it is essential than engineers have a toolbox of modeling techniques that can be used to model nonlinear engineering systems.

Such a set of basic numerical methods is the topic of this book. For each subject area treated, nonlinear models are incorporated into the discussion from the very beginning and linear models are simply treated as special cases of more general nonlinear models.

This is a basic and fundamental difference in this book from most books on numerical methods.

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Python scripting for computational science

There are many books on the use of numerical methods for solving engineering problems and for modeling of engineering artifacts. In addition there are many styles of such presentations ranging from books with a major emphasis on theory to books with an emphasis on applications. The purpose of this book is hopefully to present a somewhat different approach to the use of numerical methods for - gineering applications. Engineering models are in general nonlinear models where the response of some appropriate engineering variable depends in a nonlinear manner on the - plication of some independent parameter. It is certainly true that for many types of engineering models it is sufficient to approximate the real physical world by some linear model.

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