This book provides a practical introduction to using computational (or numerical) methods to solve physics problems using the Python programming language, including differential equations, Fourier transforms, Monte Carlo methods, and data analysis.
It is designed with a two-level approach: topics are introduced at the lowest level, and readers encounter the simplest examples of coding the algorithm themselves before a second level introduced by the problems allows the reader to use library models and take their understanding to a higher level.
The book does not teach Python programming as students traditionally have already learnt those skills before studying computational methods, but it instead teaches readers to apply their knowledge to solve realistic physics problems.
The book is aimed at advanced undergraduate or beginning graduate students in physics or engineering. A junior-level university (or college) physics and mathematics background is assumed. But readers will not be prevented from understanding or applying numerical methods because of a lack of knowledge in a specific physics area.
Key features:
Explores a wide spectrum of topics, from classical numerical methods to solving ordinary and partial differential equations of physics, plus spectral methods, data analysis, and Monte Carlo methods.Includes a chapter on data analysis and statistics, not traditionally covered in related titles on computational methods for scientists.Chapters are accompanied by problems and worked solutions (discussions, example code and output). Readers can access the full set of solutions under the support materials tab at: http://www.routledge.com/9781041116288.
This book provides a practical introduction to using computational (or numerical) methods to solve physics problems using Python, including differential equations, Fourier transforms, Monte Carlo methods, and data analysis. The book is aimed at advanced undergraduate or beginning graduate students in physics or engineering.
Chapter 1 - Preliminaries,
Chapter 2 - Classic Numerical Methods,
Chapter 3 - Differential Equations,
Chapter 4 - Fourier Transforms,
Chapter 5
- Monte Carlo Methods,
Chapter 6 - Data Analysis, Appendix A - Matplotlib
Style Sheet, Appendix B - Data for Problems.
Doug Gingrich is a Professor at the University of Alberta, Canada. He obtained his PhD from the University of Toronto and has been teaching physics for over 30 years at the University of Alberta. His main research is in experimental particle physics, where he is an author of over 1700 peer-reviewed journal articles in the fields of particle physics, gravitation, astronomy, and electronics. The publications range from single author to thousands of co-authors. He has been using computers, and a multitude of programming languages, to solve physics problems since computers were available to science students. He is now actively employing Python in statistical data analysis in particle physics and numerical solutions in gravity.
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