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).

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Yes, you can access Computational Physics by Rubin H. Landau,Manuel J Páez,Cristian C. Bordeianu,Manuel J. Páez in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Mathematical & Computational Physics. We have over one million books available in our catalogue for you to explore.

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Physical Sciences

Subtopic

Mathematical & Computational Physics

Cover
Table of Contents
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Title
Authors
Copyright
Dedication
Preface
Acknowledgments
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: Optimization, 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: Shocks Waves and Solitons
25: Fluid Dynamics
26: Integral Equations of Quantum Mechanics
Appendix A: Codes, Applets, and Animations
Bibliography
Index
End User License Agreement

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