Vasilis Pagonis, Christopher Wayne Kulp

Mathematical Methods using Python (eBook, ePUB)

Applications in Physics and Engineering

• Format: ePub

Produktbeschreibung

This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 520
• Erscheinungstermin: 14. Mai 2024
• Englisch
• ISBN-13: 9781040023051
• Artikelnr.: 70188846

Autorenporträt

Vasilis Pagonis is Professor of Physics Emeritus at McDaniel College, Maryland, USA. His research area is applications of thermally and optically stimulated luminescence. He taught courses in mathematical physics, classical and quantum mechanics, analog and digital electronics and numerous general science courses. Dr. Pagonis' resume lists more than 200 peer-reviewed publications in international journals. He is currently associate editor of the journal Radiation Measurements. He is co-author with Christopher Kulp of the undergraduate textbook "Classical Mechanics: a computational approach, with examples in Python and Mathematica" (CRC Press, 2020). He has also co-authored four graduate level textbooks in the field of luminescence dosimetry, and most recently published the book "Luminescence Signal analysis using Python" (Springer, 2022).

Christopher Kulp is the John P. Graham Teaching Professor of Physics at Lycoming College. He has been teaching undergraduate physics at all levels for 20 years. Dr. Kulp's research focuses on modelling complex systems, time series analysis, and machine learning. He has published 30 peer-reviewed papers in international journals, many of which include student co-authors. He is also co-author of the undergraduate textbook "Classical Mechanics: a computational approach, with examples in Python and Mathematica" (CRC Press, 2020).

Inhaltsangabe

Chapter 1: Introduction to Python. Chapter 2: Differentiation. Chapter 3: Integration. Chapter 4: Vectors. Chapter 5: Multiple Integrals. Chapter 6: Complex Numbers. Chapter 7: Matrices. Chapter 8: Vector Analysis. Chapter 9: Vector Spaces. Chapter 10: Ordinary Differential Equations. Chapter 11: Partial Differential Equations. Chapter 12: Analysis of Nonlinear Systems. Chapter 13: Analysis of Experimental Data. Further Reading and Additional Resources. Index.