Mathematical Physics Using Python
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Mathematical Physics Using Python
"This advanced undergraduate textbook provides a practical, pedagogical lead introduction to utilizing Python for Mathematical Physics and Computational Physics courses. Both analytical and computational example problems are integrated from its start, in addition to featuring end of chapter problems, designed to help students hone their skills in mathematical physics techniques, computer programming, and in numerical analysis. It places much less emphasis on mathematical proofs, and more emphasis on how to use computers for both numerical and symbolic calculations. This book will, therefore, provide both students and instructors with a clear presentation of the typical topics covered in a Mathematical Physics course and will present an accessible and practical instruction on how to use computational techniques to solve physics problems, by using the Python programming language. Students using the textbook will solve physics problems in three different ways: (a) Using the traditional pen-and-paper methods (b) Using scientific numerical techniques with the Python packages NumPy and SciPy (c) Using the Symbolic Python packages (e.g. SymPy). The book is accompanied by a dedicated GitHub website, which will contain all sample code used in the examples. In the same website, links will be provided for the many available resources online that a student can use in order to learn about specific Python topics. A solutions manual is also available for instructors using the textbook in their course"--
Numerical Methods in Physics with Python
Author: Alex Gezerlis
language: en
Publisher: Cambridge University Press
Release Date: 2023-07-20
A standalone text on computational physics combining idiomatic Python, foundational numerical methods, and physics applications.
Computational Physics
The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming. This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations. The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).