Computational Physics: Problem Solving with Python 3rd Edition
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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).
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Editorial Reviews
From the Inside Flap
The important aspects of computational modelling is the combination of science, mathematics and computation. Programming is part of that, and in this book the authors employ Python, which is considered as one of the easiest and most accessible language for beginning programming, and commonly used for interactive and exploratory computations in scientific research.
From the contents:
Computing software basics and Python libraries
Errors and uncertainties in computations
Monte Carlo: Randomness, walks, decays, thermodynamics
Differentiation, integration, matrix computing
Trial-and-error searching and data fitting
Solving ordinary differential equations with applications
High-performance hardware and programming
Fourier, wavelet and principal component analyses
Nonlinear dynamics
Fractals and Statistical growth models
Molecular dynamics
Partial Differential Equations: heat, waves, E-M, quantum wavepackets
Electrostatics via finite elements
Shock waves, solitons and fluid dynamics
Feynman path integrals and integral equations of quantum mecha
From the Back Cover
The important aspects of computational modelling is the combination of science, mathematics and computation. Programming is part of that, and in this book the authors employ Python, which is considered as one of the easiest and most accessible language for beginning programming, and commonly used for interactive and exploratory computations in scientific research.
From the contents:
Computing software basics and Python libraries
Errors and uncertainties in computations
Monte Carlo: Randomness, walks, decays, thermodynamics
Differentiation, integration, matrix computing
Trial-and-error searching and data fitting
Solving ordinary differential equations with applications
High-performance hardware and programming
Fourier, wavelet and principal component analyses
Nonlinear dynamics
Fractals and Statistical growth models
Molecular dynamics
Partial Differential Equations: heat, waves, E-M, quantum wavepackets
Electrostatics via finite elements
Shock waves, solitons and fluid dynamics
Feynman path integrals and integral equations of quantum mecha
About the Author
Manuel J. Paez is a professor in the Department of Physics at the University of Antioquia in Medellin, Colombia. He has been teaching courses in Modern Physics, Nuclear Physics, Computational Physics, Mathematical Physics as well as programming in Fortran, Pascal and C languages. He and Professor Landau have conducted pioneering computational investigations in the interactions of mesons and nucleons with nuclei.
Cristian C. Bordeianu teaches Physics and Computer Science at the Military College "?tefan cel Mare" in Campulung Moldovenesc, Romania. He has over twenty years of experience in developing educational software for high school and university curricula. He is winner of the 2008 Undergraduate Computational Engineering and Science Award by the US Department of Energy and the Krell Institute. His current research interests include chaotic dynamics in nuclear multifragmentation and plasma of quarks and gluons.
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Product details
- Publisher : Wiley-VCH; 3rd edition (September 8, 2015)
- Language : English
- Paperback : 644 pages
- ISBN-10 : 3527413154
- ISBN-13 : 978-3527413157
- Item Weight : 2.76 pounds
- Dimensions : 6.7 x 1.3 x 9.4 inches
- Best Sellers Rank: #837,667 in Books (See Top 100 in Books)
- #491 in Mathematical Physics (Books)
- #974 in Python Programming
- #1,238 in Physics (Books)
- Customer Reviews:
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I got this book to have a play about with Python and its graphing libraries, however this book makes it very diffiuclt to get started, there are some early chapters on fairly basic Python and calculation precision but you're going to need to look elsewhere for an introduction to Python.
Also where's the code - I assume you're not supposed to type it, but I can't find it on the website. Then, there's the Python 2/3 problem - this code is written in Python 2 which is again a little bizarre, surely a new book in 2016 should be using Python 3.
So overall more difficult to get started than I hoped, and a bit of a missed opportunity - there's not much for anyone doing financial calculations either.
The books starts with a fairly gentle introduction to programming and general computing considerations - limits of numerical accuracy, errors, etc. - and has a long list of Python libraries required for the solutions in the book - these include the ubiquitous NumPy library and a number of graphing libraries. Installation and a very quick guide to using Python is included. Python is probably the ideal language for this sort of book as it is readily available across all platforms including the Raspberry PI and has a very large set of libraries aimed at mathematical computing.
The first real exercises are on Monte Carlo simulations for random walks, moving on to protein folding simulations and decay simulations (with Geiger-counter sound simulation no less). From then on more involved subjects are covered, from methods of solving differential equation and integral problems, through fast-fourier transforms and finite element methods.
The chapters (10 and 11) on high performance computing and parallelism are particularly interesting from a general computing point of view, but the book really shines with its physical examples, heat-flow, chaotic systems and Schroedinger wave equation simulations to name a few: I particularly liked the chapter on fractals.
The book is not without its faults: in this edition there are a few mathematical errors that I spotted as I was reading through so, presumably, I would assume there are many more that I haven't spotted; I haven't yet noticed any coding errors in the examples included in the book although I'm a very long way from trying most of them. The code examples are generally well written and so readily translatable to other languages if you prefer; although I'd prefer a bigger font and a clearer presentation (the code is black text on grey). The quality of print is generally very good and clear; although the exercises are not very clearly demarcated from the rest of the text which can be a cause of occasional confusion.
Overall a really good and through examination of the field of computational physics.
It is split into 26 chapters and covers a wide range of topics:
1) Introduction
2) Computing software basics
3) Errors and uncertainties in computations
4) Monte Carlo: randomness, walks and decays
5) Differentiation and integration
6) Matrix computing
7) Trial-and-error searching and data fitting
8) Solving differential equations: nonlinear oscillations
9) ODE applications: Eigenvalues, scattering and projectiles
10) High-performance hardware and parallel computers
11) Applied HPC: Opimiation, tuning and GPU programming
12) Fourier analysis: signals and filters
13) Wavelet and principal components analyses: nonstationary signals and data compression
14) Nonlinear population dynamics
15) Continuous nonlinear dynamics
16) Fractals and statistical growth models
17) Thermodynamic simulations and Feynman path integrals
18) Molecular dynamics simulations
19) PDE review and electrostatics via finite differences and electrostatics via finite differences
20) Heat flow via time stepping
21) Wave equations I: strings and membranes
22) Wave equations II: Quantum packets and electromagnetic
23) Electrostatics via finite elements
24) Shock waves and solitons
25) Fluid dynamics
26) Integral equations of quantum mechanics
The book is well written in a simple but not condescending manner. I found it really easy to follow, the structure of the book is good and it covers a lot of topics (as you can see by the chapter list). It uses problems and examples that scientists would encounter or wish to explore. I also like the chapters which explain how computer hardware works, which isn’t always obvious to people like myself without an IT backgrounds but it is good they don’t assume everyone knows this.
This is an applied book rather than theoretical so it focuses on problems and how you can use python to solve them. For me this is ideal as I learn and work better using practical examples and step by step guides.
I highly recommend this book for anyone interested in using Python for science.
Similarly, many scientists scripts in Python ignore object-oriented principles - which is fine for single-purpose scripts, but less helpful if you want to implement a library for others to use, so it puts some time into the OO principles.
In the time since getting the book I haven't delved much into the sections on integration, differentiation, etc, but they're also important areas. Similarly Monte Carlo simulation is widely used in both physics and finance, so the coverage is useful, and appears to aim to explain the process, purpose and construction of such simulations.
The writing style is moderately academic - it's dry, but not impenetrable. I'm generally happier to see slightly more 'chatty' books, not least because they tend to provide more anecdotes that outline where the techniques have been useful or common pitfalls that the author(s) have experienced. It loses a star as a result. However, it remains a reasonably clearly written piece which provides a substantial amount of detail about a selection or core topics.








