Norbert Schorghofer

Lessons in Scientific Computing (eBook, PDF)

Numerical Mathematics, Computer Technology, and Scientific Discovery

• Format: PDF

Produktbeschreibung

Taking an interdisciplinary approach, this new book provides a modern introduction to scientific computing, exploring numerical methods, computer technology, and their interconnections, which are treated with the goal of facilitating scientific research across all disciplines. Each chapter provides an insightful lesson and viewpoints from several subject areas are often compounded within a single chapter. Written with an eye on usefulness, longevity, and breadth, Lessons in Scientific Computing will serve as a "one stop shop" for students taking a unified course in scientific computing, or seeking a single cohesive text spanning multiple courses.

Features:

• Provides a unique combination of numerical analysis, computer programming, and computer hardware in a single text
• Includes essential topics such as numerical methods, approximation theory, parallel computing, algorithms, and examples of computational discoveries in science
• Not wedded to a specific programming language

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 204
• Erscheinungstermin: 25. September 2018
• Englisch
• ISBN-13: 9781351609814
• Artikelnr.: 54254573

Autorenporträt

Norbert Schörghofer is a Senior Scientist at the Planetary Science Institute and lives in Honolulu, Hawaii. After earning degrees in physics from the University of Vienna and the University of Chicago, he held visiting positions at MIT and Caltech, before moving to the University of Hawaii. His research areas are scientific modelling, planetary science, and astrogeophysics. He has published over 60 peer reviewed publications and has been a reviewer for 30 journals. His research has been featured in New Scientist, National Geographic Magazine, Astronomy Magazine, Huffington Post, and other mass media.

Inhaltsangabe

1. Analytical and Numerical Solutions 2. A Few Concepts from Numerical
Analysis 3. Roundoff and Number Representation 4. Programming Languages and
Tools 5. Sample Problems; Building Conclusions 6. Approximation Theory 7.
Other Common Computational Methods 8. Performance Basics and Computer
Architectures 9. High-Performance and Parallel Computing 10. The Operation
Count; Numerical Linear Algebra 11. Random Numbers and Stochastic Methods
12. Algorithms, Data Structures, and Complexity 13. Data 14. Building
Programs for Computation and Data Analysis 15. Crash Course on Partial
Differential Equiations 16. Reformulated Problems