Computational Mathematics (Hardcover)

Dimitrios Mitsotakis

ISBN 10: 1032262397 / ISBN 13: 9781032262390

Published by Taylor & Francis Ltd, London, 2023

About this Item

Hardcover. 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, as well as advantages and pitfalls of classical numerical methods alongside with computer code and experiments in Python. Each chapter closes with modern applications in physics, engineering, and computer science.Features:No previous experience in Python is required. Includes simplified computer code for fast-paced learning and transferable skills development. Includes practical problems ideal for project assignments and distance learning. Presents both intuitive and rigorous faces of modern scientific computing. Provides an introduction to neural networks and machine learning. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781032262390

Bibliographic Details

• Title: Computational Mathematics (Hardcover)
• Publisher: Taylor & Francis Ltd, London
• Publication Date: 2023
• Language: English
• ISBN 10: 1032262397
• ISBN 13: 9781032262390
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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, as well as advantages and pitfalls of classical numerical methods alongside with computer code and experiments in Python. Each chapter closes with modern applications in physics, engineering, and computer science.

Features:

• No previous experience in Python is required.
• Includes simplified computer code for fast-paced learning and transferable skills development.
• Includes practical problems ideal for project assignments and distance learning.
• Presents both intuitive and rigorous faces of modern scientific computing.
• Provides an introduction to neural networks and machine learning.

About the Author

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.