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Finite Difference Computing with Exponential Decay Models
Finite Difference Computing with Exponential Decay Models
Author(s): Hans Petter Langtangen
Open Access: BY-NC 4.0
Publication Date  Available in all formats
Publisher: Springer Nature
ISBN: 9783319294391
Pages: 200
https://doi.org/10.1007/978-3-319-29439-1

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ISBN: 9783319294391

Description

Table of contents

Keyword(s): Software Engineering, Computational Science and Engineering, Programming Techniques
This text provides a very simple, initial introduction to the complete scientific computing pipeline: models, discretization, algorithms, programming, verification, and visualization. The pedagogical strategy is to use one case study – an ordinary differential equation describing exponential decay processes – to illustrate fundamental concepts in mathematics and computer science. The book is easy to read and only requires a command of one-variable calculus and some very basic knowledge about computer programming. Contrary to similar texts on numerical methods and programming, this text has a much stronger focus on implementation and teaches testing and software engineering in particular.
Description
Subject(s): Software Engineering, Computational Science and Engineering, Programming Techniques
This text provides a very simple, initial introduction to the complete scientific computing pipeline: models, discretization, algorithms, programming, verification, and visualization. The pedagogical strategy is to use one case study – an ordinary differential equation describing exponential decay processes – to illustrate fundamental concepts in mathematics and computer science. The book is easy to read and only requires a command of one-variable calculus and some very basic knowledge about computer programming. Contrary to similar texts on numerical methods and programming, this text has a much stronger focus on implementation and teaches testing and software engineering in particular.
Table of contents
  • Cover
  • Front Matter
  • 1. Algorithms and Implementations
  • 2. Analysis
  • 3. Generalizations
  • 4. Models
  • 5. Scientific Software Engineering
  • Back Matter

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