His current research interests focus on using machine learning to analyze time series and model complex systems. He maintains an active undergraduate research group at Lycoming College, having mentored nearly 50 undergraduate students during his career so far. Much of his work focuses on distinguishing between chaotic and stochastic behavior in time series data. More than 10 of his publications have undergraduate co-authors. He has published more than 20 publications in peer-reviewed journals and conference proceedings and has written two book chapters. Kulp's research interests focus on the fields of nonlinear dynamics, nonlinear time series analysis, and complex systems. Chris has a life-long passion for teaching and, in addition to teaching at several colleges and universities, he has also taught martial arts, high school science, science seminars for K-12 teachers, and a summer kindergarten math program. 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. Online resources containing specific example codes to help students learn computational methods and write their own algorithmsĪ solutions manual is available via the Routledge Instructor Hub and extra code is available via the Support Material tabĭr.
Numerous example problems using both analytical and computational methods, as well as explanations as to how and why specific techniques were used Gives readers the "big picture" of classical mechanics and the importance of computation in the solution of problems in physics It is an ideal textbook for undergraduate students in physics, mathematics, and engineering studying classical mechanics. The methods presented in this book can then be used by students when solving problems in other fields both within and outside of physics. End-of-chapter problems allow students to hone their skills in problem solving with and without the use of a computer. The example problems teach readers both analytical methods and how to use computer algebra systems and computer programming to solve problems in classical mechanics. In addition to providing clear and thorough coverage of key topics, this textbook includes integrated instructions and treatments of computation.įull of pedagogy, it contains both analytical and computational example problems within the body of each chapter. 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.
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