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Mathematical Methods using Python: Applications in Physics and Engineering
Mathematical Methods using Python: Applications in Physics and Engineering
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Excellent- More about Mathematical Methods using Python: Applications in Physics and Engineering
This advanced undergraduate textbook provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. It focuses on how to use computers for symbolic and numerical calculations, with 182 extensively documented coding examples based on topics that students will encounter in their advanced courses. The book is accompanied by a dedicated GitHub website and a solutions manual for instructors.
Format: Hardback
Length: 488 pages
Publication date: 14 May 2024
Publisher: Taylor & Francis Ltd
This advanced undergraduate textbook presents a novel approach to teaching mathematical methods for scientists and engineers. It offers a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. From the outset, both analytical and computational examples are integrated. Each chapter concludes with a set of problems designed to help students refine their skills in mathematical techniques, computer programming, and numerical analysis. The book places less emphasis on mathematical proofs and more on how to use computers for both symbolic and numerical calculations. It contains 182 extensively documented coding examples, based on topics that students will encounter in their advanced courses in Mechanics, Electronics, Optics, Electromagnetism, Quantum Mechanics, and more.
An introductory chapter provides students with a crash course in Python programming and the most commonly used libraries (SymPy, NumPy, SciPy, Matplotlib). This is followed by chapters dedicated to differentiation, integration, vectors, and multiple integration techniques. The next group of chapters covers complex numbers, matrices, vector analysis, and vector spaces. Extensive chapters cover ordinary and partial differential equations, followed by chapters on nonlinear systems and on the analysis of experimental data using linear and nonlinear regression techniques, Fourier transforms, binomial and Gaussian distributions.
The book is accompanied by a dedicated GitHub website, which contains all codes from the book in the form of ready-to-run Jupyter notebooks. A detailed solutions manual is also available for instructors using the textbook in their courses.
Key Features:
A unique teaching approach that seamlessly merges mathematical methods and Python programming skills.
A comprehensive coverage of mathematical topics, including differential equations, matrices, vector analysis, and more.
Extensive coding examples and exercises to reinforce learning and practice.
A focus on practical applications in various scientific and engineering fields.
A dedicated GitHub website with all codes and solutions for easy access and reference.
An instructor's solutions manual for classroom use.
This textbook is an invaluable resource for undergraduate students studying mathematical methods and Python programming, providing them with the skills and knowledge necessary to excel in their advanced courses and pursue careers in science, engineering, and mathematics.
Weight: 1132g
Dimension: 186 x 263 x 35 (mm)
ISBN-13: 9781032278360
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