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

Computational Physics (eBook, ePUB)

Problem Solving with Python

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Wiley-VCH
• Seitenzahl: 1027
• Erscheinungstermin: 20. März 2024
• Englisch
• ISBN-13: 9783527843312
• Artikelnr.: 70236455

Autorenporträt

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.

Inhaltsangabe

1. Computational Science Basics
2. Errors & Uncertainties in Computations
3. Visualization Tools
4. Python Object-Oriented Programs: Impedance & Batons
5. Monte Carlo Simulations (Nonthermal)
6. Integration
7. Differentiation & Searching
8. Matrix Equation Solutions; Data Fitting
9. Differential Equation Applications
10.Fourier Analysis: Signals and Filters
11.Wavelet Analysis & Data Compression
12.Discrete & Continuous Nonlinear Dynamics
13.Fractals & Statistical Growth
14.HPC Hardware, Tuning, Parallel Computing
15.Thermodynamic Simulations, Quantum Path Integration
16.Simulating Matter with Molecular Dynamics
17.PDEs for Electrostatics & Heat Flow
18.PDE Waves: String, Quantum Packet, E&M 1
19.Solitons & Computational Fluid Dynamics
20.Integral Equations in Quantum Mechanics
A. Glossary
B. Installing Python, Matplotlib, NumPy
C. Software Directories
D. Compression via DWT with Thresholding

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Preface

PART I. BASICS
Introduction
Software Basics
Errors & Uncertainties
Monte Carlo Simulations
Differentiation & Integration
Trial-and-Error Searching & Data Fitting
Matrix Computing and N-D Searching
Differential Equations & Nonlinear Oscillations

PART II. DATA SCIENCE
Fourier Analyses
Wavelet & Principal Components Analysis
Neural Networks & Machine Learning
Quantum Computing

PART III. APPLICATIONS
ODE Applications; Eigenvalues, Scattering, Trajectories
Fractals & Statistical Growth Models
Nonlinear Population Dynamics
Nonlinear Dynamics of Continuous Systems
Thermodynamics Simulations & Fenyman Path Integrals
Molecular Dynamics Simulations
General Relativity
Integral Equations

PART IV. PDE APPLICATIONS
PDE Review, Electrostatics & Relaxation
Heat Flow & Leapfrogging
String & Membrane Waves
Quantum Wave Packets & EM Waves
Shock & Soliton Waves
Fluid Hydrodynamics
Finite Element Electrostatics

Appendices
Index