computational physics problem solving with python 4th edition

Computational Physics: Problem Solving with Python, 4th Edition

ISBN: 978-3-527-41425-3

Digital Evaluation Copy

Rubin H. Landau , Cristian C. Bordeianu , Manuel J. Páez

The classic in the field for more than 25 years, now with increased emphasis on data science and new chapters on quantum computing, machine learning (AI), and general relativity

Computational physics combines physics, applied mathematics, and computer science in a cutting-edge multidisciplinary approach to solving realistic physical problems. It has become integral to modern physics research because of its capacity to bridge the gap between mathematical theory and real-world system behavior.

Computational Physics provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. Its philosophy is rooted in “learning by doing”, assisted by many sample programs in the popular Python programming language. The first third of the book lays the fundamentals of scientific computing, including programming basics, stable algorithms for differentiation and integration, and matrix computing. The latter two-thirds of the textbook cover more advanced topics such linear and nonlinear differential equations, chaos and fractals, Fourier analysis, nonlinear dynamics, and finite difference and finite elements methods. A particular focus in on the applications of these methods for solving realistic physical problems.

Readers of the fourth edition of Computational Physics will also find:

• An exceptionally broad range of topics, from simple matrix manipulations to intricate computations in nonlinear dynamics
• A whole suite of supplementary material: Python programs, Jupyter notebooks and videos

Computational Physics is ideal for students in physics, engineering, materials science, and any subjects drawing on applied physics.

Rubin H. Landau, PhD, is Professor Emeritus in the Department of Physics at Oregon State University, Corvallis, Oregon, USA. In his long and distinguished research career he has been instrumental in the development of computational physics as a defined subject, and founded both the Computational Physics Degree Program and the Northwest Alliance for Computational Science and Engineering.

Manuel J. Páez, PhD, is a Professor in the Department of Physics at the University of Antioquia in Medellin, Colombia. He teaches courses in both physics and programming, and he and Professor Landau have collaborated on pathbreaking computational physics investigations.

Cristian C. Bordeianu, PhD, taught Physics and Computer Science at the Military College “Stefan cel Mare,” Campulung Moldovenesc, Romania.

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Computational Physics 4th Edition Problem Solving with Python

• Author(s) Rubin H. Landau; Manuel J. Páez; Cristian C. Bordeianu
• Publisher Wiley-Blackwell

Print ISBN 9783527414253, 3527414258

Etext isbn 9783527843312, 3527843310.

• Edition 4th
• Copyright 2024
• Available from $ 100.00 USD SKU: 9783527843312

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Computational Physics: Problem Solving with Python 4th Edition is written by Rubin H. Landau; Manuel J. Páez; Cristian C. Bordeianu and published by Wiley-Blackwell. The Digital and eTextbook ISBNs for Computational Physics are 9783527843312, 3527843310 and the print ISBNs are 9783527414253, 3527414258. Save up to 80% versus print by going digital with VitalSource.

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• Introduction
• Computing Software Basics
• Errors and Uncertainties in Computations
• Monte Carlo: Randomness, Walks, and Decays
• Differentiation and Integration
• Matrix Computing
• Trial-and-Error Searching and Data Fitting
• Solving Differential Equations: Nonlinear Oscillations
• ODE Applications: Eigenvalues, Scattering, and Projectiles
• High-Performance Hardware and Parallel Computers
• Applied HPC: Optimization, Tuning, and GPU Programming
• Fourier Analysis: Signals and Filters
• Wavelet and Principal Components Analyses: Nonstationary Signals and Data Compression
• Nonlinear Population Dynamics
• Continuous Nonlinear Dynamics
• Fractals and Statistical Growth Models
• Thermodynamic Simulations and Feynman Path Integrals
• Molecular Dynamics Simulations
• PDE Reviewand Electrostatics via Finite Differences and Electrostatics via Finite Differences
• Heat Flow via Time Stepping
• Wave Equations I: Strings and Membranes
• Wave Equations II: QuantumPackets and Electromagnetic
• Electrostatics via Finite Elements
• Shocks Waves and Solitons
• Fluid Dynamics
• Integral Equations of Quantum Mechanics
• Appendix A Codes, Applets, and Animations

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The classic in the field for more than 25 years, now with increased emphasis on data science and new chapters on quantum computing, machine learning (AI), and general relativity

• An exceptionally broad range of topics, from simple matrix manipulations to intricate computations in nonlinear dynamics
• A whole suite of supplementary material: Python programs, Jupyter notebooks and videos

Computational Physics is ideal for students in physics, engineering, materials science, and any subjects drawing on applied physics.

• ISBN-10 3527414258
• ISBN-13 978-3527414253
• Edition 4th
• Publisher Wiley-VCH
• Publication date 17 April 2024
• Language English
• Dimensions 17 x 3.04 x 24.4 cm
• Print length 592 pages
• See all details

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PHY432 — Computational Methods in Physics | Learning | Beckstein Lab

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PHY432 — Computational Methods in Physics

The course provides an introduction to using the computer as a tool to solve problems in physics. Students will learn to analyze problems, select appropriate numerical algorithms, implement them using Python , a programming language widely used in scientific computing, and critically evaluate their numerical results. Problems will be drawn from diverse areas of physics.

This class starts in Spring 2022 as PHY 432 . (The class was running as PHY 494 from Spring 2016 to Spring 2021.)

For a brief overview, see this page and the Syllabus .

The topics of the class include applications in electrostatics and heat transfer as well as applications such as writing a molecular dynamics code to simulate liquid argon or simulating the lenses in an transmission electron microscope.

Students worked on Final Projects in 2019 as diverse as path integral Monte Carlo, simulations of celestial bodies such as Jupiter’s Trojan asteroids or the solar system, electrons in J J Thompson’s classical experiment, molecular dynamics of organic molecules or rock salt crystals, or the collective behavior of self driving cars. The Final Projects in 2016 included navigation in the solar system, real time digital soundeffect processing, and agent-based modeling such as generating a winning strategy for Black Jack.

Time and requirements

All times and dates to be confirmed.

• Spring 2022: listed as PHY 432
• TTh 1:30pm — 2:45pm
• PHY 252 is a pre- or co-requisite.
• Programming experience as taught in PHY 202 (online) or equivalent required.

If possible, bring your own laptop (Linux, MacOS, Windows).

Course Description

The course provides a practical introduction to using the computer as a tool to solve problems in physics. Students will learn how to work in a scientific computing environment, to analyze a physical problem, select appropriate numerical algorithms to solve the problem, and to implement them. The course will introduce the students to computer graphics and object oriented design. Students will work in teams, critically evaluate their approaches and results, and present them in a professional manner to their peers. The instructor will introduce problems and guide students to their solution.

This is a three-credit hour course. It will be taught in a computer laboratory/studio setting. The emphasis is on practical work , with the instructor initially introducing the problem, and the students then pursuing pre-structured programming exercises and projects. Assessment will primarily focus on projects, including group projects, in which students solve a problem as a small team and present their work as a short report or as a presentation.

In order to facilitate the interactive setting, the capacity will be capped at 24 students. The class is designed to work equally well for remote and in-person students.

PHY 252 is a co-requisite.

Basic programming experience in Python is required; the Online class PHY 202 (by the same instructor) is recommended and can be taken in the preceding Spring, Summer, or Fall semester.

Learning outcomes

Students will learn to program computers in order to solve physical problems. In particular, they will be able to write object oriented code in the open source Python programming language, which is widely used in science and engineering and in some of the biggest tech companies such as Google.

Students will also learn how to solve problems in teams and to communicate their work clearly and effectively.

By the completion of the course, students should be able to quickly apply their knowledge to problems they encounter in other courses and experimental and theoretical research projects.

List of topics

• Working in a scientific computing environment: basic Linux and command line.
• Version control with git
• Programming in Python (including object oriented programming and use of the numpy library).
• Plotting and visualization with matplotlib .
• Numerics fundamentals: numbers, errors
• Differentiation and Integration
• Ordinary differential equations ( ODE ), integration of equations of motion
• Linear algebra (vectors, matrices, eigenvalue problems, SVD )
• Root finding and optimization
• Partial differential equations ( PDE ) e.g. Poisson’s equation, diffusion equation, wave equation
• Monte Carlo methods (importance sampling, Ising model)

For mode details see the course modules overview .

Books and Resources

Recommended text books.

• Computational Modeling and Visualization of Physical Systems with Python , Jay Wang (2016) Wiley- VCH . ISBN : 978-1-119-17918-4
• Computational Physics: Problem Solving with Python, 3rd Edition , Rubin H. Landau, Manuel J. Páez, Cristian C. Bordeianu. (2015) Wiley- VCH . ISBN : 978-3-527-41315-7

Additional books and online resources

• Effective Computation in Physics. Field Guide to Research with Python , Anthony Scopatz and Kathryn D. Huff. (2015) O’Reilly
• A Survey of Computational Physics , Rubin Landau, Manuel J. Páez, and Cristian C. Bordeianu. (2011) Princeton University Press. Free online ComPADRE edition and PDF
• Software Carpentry (especially the lessons on the Unix Shell , Version Control with Git , and Programming with Python )

Course website

• Course page py4phy.github.io/PHY432
• PHY432 GitHub site

For a number of lessons in Spring 2020, videos were created, which you can look at to get a feel for the class.

Discuss: “PHY432 — Computational Methods in Physics”

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This repo contains a collection of Python programs to solve Physics problems. The programs employ numerical methods to solve a range of physics problems from classical mechanics to quantum mechanics and beyond.

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Bibliometrics & citations, view options, recommendations, auxiliary equation method for time-fractional differential equations with conformable derivative.

In this paper, the auxiliary equation method is applied to obtain analytical solutions of (2 + 1)-dimensional time-fractional Zoomeron equation and the time-fractional third order modified KdV equation in the sense of the conformable ...

Differential operator multiplication method for fractional differential equations

Fractional derivatives play a very important role in modeling physical phenomena involving long-range correlation effects. However, they raise challenges of computational cost and memory storage requirements when solved using current well developed ...

Fractional partial differential equations and modified Riemann-Liouville derivative new methods for solution

The paper deals with the solution of some fractional partial differential equations obtained by substituting modified Riemann-Liouville derivatives for the customary derivatives. This derivative is introduced to avoid using the so-called Caputo ...

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• Time-fractional differential equations
• Anomalous diffusion
• Mittag-Leffler function
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Computational Physics: Problem Solving with Python 3rd Edition

• ISBN-10 3527413154
• ISBN-13 978-3527413157
• Edition 3rd
• Publisher Wiley-VCH
• Publication date September 8, 2015
• Language English
• Dimensions 6.7 x 1.3 x 9.4 inches
• Print length 644 pages
• See all details

Editorial Reviews

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

Product details.

• Publisher ‏ : ‎ Wiley-VCH; 3rd edition (September 8, 2015)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 644 pages
• ISBN-10 ‏ : ‎ 3527413154
• ISBN-13 ‏ : ‎ 978-3527413157
• Item Weight ‏ : ‎ 2.7 pounds
• Dimensions ‏ : ‎ 6.7 x 1.3 x 9.4 inches
• #844 in Mathematical Physics (Books)
• #1,862 in Python Programming
• #68,764 in Unknown

About the author

Rubin h. landau.

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