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.
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: https: //github.com/vpagonis/Classical_Mechanics_2nd_Edition
Christopher W. Kulp received his PhD in Physics from the College of William and Mary in 2004 and is currently a Professor of Physics at Lycoming College, where he teaches physics at all levels. Dr. Kulp's research interests focus on the fields of nonlinear dynamics and complex systems. He has published more than 20 publications in peer-reviewed journals and conference proceedings and has written two book chapters. More than 10 of his publications have undergraduate co-authors. Much of his work focuses on distinguishing between chaotic and stochastic behaviour in time series data. His current research interests focus on using machine learning to analyse time series and model complex systems.
Vasilis Pagonis is Professor of Physics Emeritus at McDaniel College, Maryland, where he taught undergraduate courses and did research for 36 years. He is currently a Senior Associate Editor of the international journal "Radiation Measurements". His research areas of interest is luminescence dosimetry, and applications of thermally and optically stimulated luminescence (TL and OSL). He has taught courses in classical and quantum mechanics, analog and digital electronics and mathematical physics, as well as numerous general science courses. Dr. Pagonis' resume lists more than 200 peer-reviewed publications in international journals. He is the co-author with Dr Kulp of the textbook "Mathematical methods using Python" (CRC, 2024). He has also co-authored five graduate level books in the field of luminescence dosimetry.
