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Total Size:
34.5 MB
Info Hash:
93AF79635631D3FE9D3CFD3D6B372FD6620B4F06
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Added:
Aug. 17, 2025, 2:37 a.m.
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(Last updated: Aug. 17, 2025, 2:39 a.m.)
| File | Size |
|---|---|
| Code.zip | 4.9 MB |
| Kulp C. Classical Mechanics. A Computational Approach with Examples...2ed 2026.pdf | 29.6 MB |
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34.5 MB
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2025-08-17
| Uploaded by andryold1 | Size 34.5 MB | Health [ 103 /8 ] | Added 2025-08-17 |
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SOURCE: Kulp C. Classical Mechanics. A Computational Approach with Examples...2ed 2026
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MEDIAINFO
Textbook in PDF format Classical Mechanics: A Computational Approach with Examples using Python and Mathematica provides a unique, contemporary introduction to classical mechanics, with a focus on computational methods. In addition to providing clear and thorough coverage of key topics, this textbook includes integrated instructions and treatments of computation. This newly updated and revised second edition includes two new appendices instructing the reader in both the Python and Mathematica languages. All worked example problems in the second edition contain both Python and Mathematica code. New end-of-chapter problems explore the application of computational methods to classical mechanics problems. Full of pedagogy, it contains both analytical and computational example problems within the body of each chapter. The example problems teach readers both analytical methods and how to use computer algebra systems and computer programming to solve problems in classical mechanics. End-of-chapter problems allow students to hone their skills in problem solving with and without the use of a computer. The methods presented in this book can then be used by students when solving problems in other fields both within and outside of physics. Physicists need many different computational tools to solve problems. Python is a powerful, easy-to-use language which has become a mainstay in science. We also include Mathematica, since it is a world-class computer algebra system. Like Python, it is easy to learn, but Mathematica can often handle complex algebraic manipulations better than Python. Furthermore, Mathematica often requires fewer lines of coding than Python, because of its rich set of commands. By presenting a variety of tools, we are hoping that the student will be able to choose the best tool for solving a particular problem. The prerequisites for this book are two semesters of introductory physics and two semesters of calculus. The codes in this textbook tend to favor clarity over efficiency. Experienced programmers will certainly find more efficient ways of solving the various example problems in this book. However, we believe that it is much more beneficial for the student to include a few more lines of code for the purpose of clarity, rather than to try to combine multiple lines for a more elegant algorithm. Solving the problems with algorithms that they develop themselves is a beneficial exercise for students, and it is highly recommended. It is an ideal textbook for undergraduate students in physics, mathematics, and engineering studying classical mechanics. Key Features: Gives readers the "big picture" of classical mechanics and the importance of computation in the solution of problems in physics Numerous example problems using both analytical and computational methods, as well as explanations as to how and why specific techniques were used Online resources containing specific example codes to help students learn computational methods and write their own algorithms A solutions manual is available via the Routledge Instructor Hub and all example codes in the book are available via the Support Material tab, and at the book’s GitHub page
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