Dimitrios Mitsotakis

Computational Mathematics (eBook, PDF)

An introduction to Numerical Analysis and Scientific Computing with Python

• Format: PDF

Produktbeschreibung

This textbook is a comprehensive introduction to computational mathematics and scientific computing suitable for undergraduate and postgraduate courses. It presents both practical and theoretical aspects of the subject, and advantages and pitfalls of classical numerical methods alongside with computer code and experiments in Python.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 528
• Erscheinungstermin: 19. Juni 2023
• Englisch
• ISBN-13: 9781000889437
• Artikelnr.: 68132487

Autorenporträt

Dimitrios Mitsotakis received a PhD in Mathematics in 2007 from the University of Athens. His experience with high-performance computing started while at the Edinburgh Parallel Computing Center at the University of Edinburgh. Dimitrios worked at the University Paris-Sud as a Marie Curie fellow, at the University of Minnesota as an associate postdoc and at the University of California, Merced as a Visiting Assistant Professor. Dimitrios is currently an associate professor/reader at the School of Mathematics and Statistics of Victoria University of Wellington. He has published his work in journals of numerical analysis and in more general audience journals in physics, coastal engineering, waves sciences, and in scientific computing. He develops numerical methods for the solution of equations for water waves, and he studies real-world applications such as the generation of tsunamis. Some of his main contributions are in the theory and numerical analysis of Boussinesq systems for nonlinear and dispersive water waves.

Inhaltsangabe

1. Introduction to Python. 2. Matrices and Python. 3. Scientific computing. 4. Calculus facts. 5. Roots of equations. 6. Interpolation and approximation. 7. Numerical integration. 8. Numerical differentiation and applications to differential equations. 9. Numerical linear algebra. 10. Best approximations. 11. Unconstrained optimization and neural networks. 12. Eigenvalue problems.