Numerical Methods in Physics with Python

Alex Gezerlis

ISBN 10: 1009303864 / ISBN 13: 9781009303866

Verlag: Cambridge University Press, 2023

Beschreibung

2nd edition. 700 pages. 10.00x7.00x1.41 inches. In Stock.

Bestandsnummer des Verkäufers __1009303864

Bibliografische Details

• Titel: Numerical Methods in Physics with Python
• Verlag: Cambridge University Press
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 1009303864
• ISBN-13: 9781009303866
• Einband: Paperback
• Auflage: 2. Auflage
• Zustand: Brand New

Über diesen Titel

Inhaltsangabe

Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.

Über die Autorin bzw. den Autor

Alex Gezerlis is Professor of Physics at the University of Guelph. Before moving to Canada, he worked in Germany, the United States, and Greece. He has received several research awards, grants, and allocations on supercomputing facilities. He has taught undergraduate and graduate courses on computational methods, as well as courses on quantum field theory, subatomic physics, and science communication.