Numerical Methods in Physics with Python PDF Download
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Author: Alex Gezerlis
Publisher: Cambridge University Press
ISBN: 1009303856
Category : Computers
Languages : en
Pages : 705
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Book Description
A standalone text on computational physics combining idiomatic Python, foundational numerical methods, and physics applications.
Author: Alex Gezerlis
Publisher: Cambridge University Press
ISBN: 1009303856
Category : Computers
Languages : en
Pages : 705
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Book Description
A standalone text on computational physics combining idiomatic Python, foundational numerical methods, and physics applications.
Author: Alejando L. Garcia
Publisher: Createspace Independent Publishing Platform
ISBN: 9781514136683
Category : Differential equations, Partial
Languages : en
Pages : 0
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Book Description
This book covers a broad spectrum of the most important, basic numerical and analytical techniques used in physics -including ordinary and partial differential equations, linear algebra, Fourier transforms, integration and probability. Now language-independent. Features attractive new 3-D graphics. Offers new and significantly revised exercises. Replaces FORTRAN listings with C++, with updated versions of the FORTRAN programs now available on-line. Devotes a third of the book to partial differential equations-e.g., Maxwell's equations, the diffusion equation, the wave equation, etc. This numerical analysis book is designed for the programmer with a physics background. Previously published by Prentice Hall / Addison-Wesley
Author: Qingkai Kong
Publisher: Academic Press
ISBN: 0128195509
Category : Technology & Engineering
Languages : en
Pages : 482
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Book Description
Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings. - Includes tips, warnings and "try this" features within each chapter to help the reader develop good programming practice - Summaries at the end of each chapter allow for quick access to important information - Includes code in Jupyter notebook format that can be directly run online
Author: Mark E. J. Newman
Publisher: Createspace Independent Publishing Platform
ISBN: 9781480145511
Category : Computational physics
Languages : en
Pages : 0
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Book Description
This book explains the fundamentals of computational physics and describes the techniques that every physicist should know, such as finite difference methods, numerical quadrature, and the fast Fourier transform. The book offers a complete introduction to the topic at the undergraduate level, and is also suitable for the advanced student or researcher. The book begins with an introduction to Python, then moves on to a step-by-step description of the techniques of computational physics, with examples ranging from simple mechanics problems to complex calculations in quantum mechanics, electromagnetism, statistical mechanics, and more.
Author: Jaan Kiusalaas
Publisher: Cambridge University Press
ISBN: 1107033853
Category : Computers
Languages : en
Pages : 437
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Book Description
Provides an introduction to numerical methods for students in engineering. It uses Python 3, an easy-to-use, high-level programming language.
Author: Titus A. Beu
Publisher: CRC Press
ISBN: 1466569670
Category : Mathematics
Languages : en
Pages : 676
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Book Description
Makes Numerical Programming More Accessible to a Wider Audience Bearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C++ utilizes the author’s many years of practical research and teaching experience to offer a systematic approach to relevant programming concepts. Adopting a practical, broad appeal, this user-friendly book offers guidance to anyone interested in using numerical programming to solve science and engineering problems. Emphasizing methods generally used in physics and engineering—from elementary methods to complex algorithms—it gradually incorporates algorithmic elements with increasing complexity. Develop a Combination of Theoretical Knowledge, Efficient Analysis Skills, and Code Design Know-How The book encourages algorithmic thinking, which is essential to numerical analysis. Establishing the fundamental numerical methods, application numerical behavior and graphical output needed to foster algorithmic reasoning, coding dexterity, and a scientific programming style, it enables readers to successfully navigate relevant algorithms, understand coding design, and develop efficient programming skills. The book incorporates real code, and includes examples and problem sets to assist in hands-on learning. Begins with an overview on approximate numbers and programming in Python and C/C++, followed by discussion of basic sorting and indexing methods, as well as portable graphic functionality Contains methods for function evaluation, solving algebraic and transcendental equations, systems of linear algebraic equations, ordinary differential equations, and eigenvalue problems Addresses approximation of tabulated functions, regression, integration of one- and multi-dimensional functions by classical and Gaussian quadratures, Monte Carlo integration techniques, generation of random variables, discretization methods for ordinary and partial differential equations, and stability analysis This text introduces platform-independent numerical programming using Python and C/C++, and appeals to advanced undergraduate and graduate students in natural sciences and engineering, researchers involved in scientific computing, and engineers carrying out applicative calculations.
Author: Rubin H. Landau
Publisher: John Wiley & Sons
ISBN: 3527413154
Category : Science
Languages : en
Pages : 647
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Book Description
The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming. This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations. The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).
Author: Robert Johansson
Publisher: Apress
ISBN: 1484242467
Category : Computers
Languages : en
Pages : 709
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Book Description
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.
Author: Giovanni Moruzzi
Publisher: Springer Nature
ISBN: 3030450279
Category : Science
Languages : en
Pages : 304
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Book Description
This book introduces the reader with little or no previous computer-programming experience to the Python programming language of interest for a physicist or a natural-sciences student. The book starts with basic interactive Python in order to acquire an introductory familiarity with the language, than tackle Python scripts (programs) of increasing complexity, that the reader is invited to run on her/his computer. All program listings are discussed in detail, and the reader is invited to experiment on what happens if some code lines are modified. The reader is introduced to Matplotlib graphics for the generation of figures representing data and function plots and, for instance, field lines. Animated function plots are also considered. A chapter is dedicated to the numerical solution of algebraic and transcendental equations, the basic mathematical principles are discussed and the available Python tools for the solution are presented. A further chapter is dedicated to the numerical solution of ordinary differential equations. This is of vital importance for the physicist, since differential equations are at the base of both classical physics (Newton’s equations) and quantum mechanics (Schroedinger’s equation). The shooting method for the numerical solution of ordinary differential equations with boundary conditions at two boundaries is also presented. Python programs for the solution of two quantum-mechanics problems are discussed as examples. Two chapters are dedicated to Tkinter graphics, which gives the user more freedom than Matplotlib, and to Tkinter animation. Programs displaying the animation of physical problems involving the solution of ordinary differential equations (for which in most cases there is no algebraic solution) in real time are presented and discussed. Finally, 3D animation is presented with Vpython.
Author: Marco Bittelli
Publisher:
ISBN: 0199683093
Category : Science
Languages : en
Pages : 461
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Book Description
This innovative study presents concepts and problems in soil physics, and provides solutions using original computer programs. It provides a close examination of physical environments of soil, including an analysis of the movement of heat, water and gases. The authors employ the programminglanguage Python, which is now widely used for numerical problem solving in the sciences. In contrast to the majority of the literature on soil physics, this text focuses on solving, not deriving, differential equations for transport. Using numerical procedures to solve differential equations allowsthe solution of quite difficult problems with fairly simple mathematical tools. Numerical methods convert differential into algebraic equations, which can be solved using conventional methods of linear algebra. Each chapter introduces a soil physics concept, and proceeds to develop computer programsto solve the equations and illustrate the points made in the discussion.Problems at the end of each chapter help the reader practise using the concepts introduced. The text is suitable for advanced undergraduates, graduates and researchers of soil physics. It employs an open source philosophy where computer code is presented, explained and discussed, and provides thereader with a full understanding of the solutions. Once mastered, the code can be adapted and expanded for the user's own models, fostering further developments. The Python tools provide a simple syntax, Object Oriented Programming techniques, powerful mathematical and numerical tools, and a userfriendly environment.
