Gingrich D. Computational Physics Using Python 2026

Textbook in PDF format 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 coding examples in this book are written using the Python programming language in Jupyter Notebooks. Although neither of these is necessary to learning computational physics, I have found Python to be the overweeningly favourite programming language of undergraduate students these days. I thus often take the occasion to briefly discuss the Python implementation of the problem. The use of Jupyter Notebooks is not necessary for this book, although I may have occasionally used some terminology like “cell” to describe a block of code. This book contains a number of examples and problems. Examples usually appear at the end of a chapter but sometime directly follow the section in which the topic is discussed. The problems appear in a dedicated section at the end of each chapter. Sometimes a problem can appear quite long. This is necessary to introduce the physics concepts leading to the problem to be solved. The length of the problem is not an indication of its difficulty. Writing code is often not done in isolation. The codes in the examples in this book is complete. It is also well known that the internet provides abundant code that solves many of the problems in this book. At the time of writing, large language models (LLM) can be used to write code for some of the problems in this book. I recommended that these codes be used as guides for the reader when writing their own programs. At the very least, the reader is asked to test and extend the available code to the problem at hand. I believe there is pedagogical value in using someone else's code which is a valuable workplace skill to develop. But the code should be understood before used. 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 book site

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