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Computational Physics

The University of Texas at Austin
Overview of Methods in Course

(Left to Right): Avalanche activity cascades in a sandpile automaton; a vortex street formed by flow past a cylinder; and Turing patterns in a reaction-diffusion model. All simulations from the course homeworks; a higher-resolution video may be viewed here

Computational Physics

Summary

Materials for computational physics course, taught by William Gilpin.

This course aims to provide a very broad survey of computational methods that are particularly relevant to modern physics research. We will aim to cover efficient algorithm design and performance analysis, traditional numerical recipes such as integration and matrix manipulation, and emerging methods in data analysis and machine learning. Our goal by the end of the class will be to feel comfortable approaching diverse, open-ended computational problems that arise during research, and to be ready to design and share new algorithms with the broader research community.

For UT students

Contents

The class website is located here. From the class website, the sidebar links to Chapters, Homeworks, Labs, and Appendices. To navigate to a specific lecture, click on the lecture title in the “Chapter” section. Within a given chapter or homework, there is a link at the top of the page to open the notebook in Google Colab.

Example Final Projects

The guidelines and rubric for the final project and presentation can be found here

Usage and improvements

If you are teaching a similar course, please feel free to use any or all of these materials. If you have any suggestions for improvements or find any errors, I would very much appreciate any feedback. Consider submitting corrections as issues or pull requests on GitHub.

For students, logistics and project questions are best posted on the classroom forum (Ed Discussions); errors in the materials should be issues on the course repository; for other issues, I can be reached via email

Requirements

This book uses Python 3, with particular dependence the following packages

Other parts of the book and projects depend on the broader Python ecosystem, including