Description
Topological Data Analysis for Scientific Visualization is a comprehensive, self-paced guide to integrating the theoretical and practical aspects of geometry and topology in the field of scientific visualization. The author has tried to show the audience how advanced mathematical tools can be used to better understand large scientific data, discover hidden patterns in complex structures, and simplify them without losing vital information. The book begins with a review of real-world applications in various fields such as astronomy and physics to clearly illustrate the importance of these methods.
In the following sections, key algorithms for topological computations, data stabilization, and their implementation are described in detail. The book strikes a good balance between theoretical mathematical topics and practical engineering applications, making it an ideal teaching and research resource for graduate students, university professors, and researchers in the fields of computer science and data visualization.
Book Features
- Providing a balanced approach between advanced mathematical theories and data visualization engineering.
- A detailed review and description of key computational algorithms for simplifying topological representations.
- Dedicating an entire chapter to examining real-world examples and concrete applications in large scientific projects.
- Self-paced and independent structure that makes the book easier to read for different audiences.
Book specifications
- Publisher: Springer
- Instructor/Author: Julien Tierny
- Number of pages: 158
- Number of chapters: 7
- Format: PDF
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User Guide
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Download link
File(s) password: www.downloadly.ir
File size
5 MB