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.
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.
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
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
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