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

Computational Physics

Problem Solving with Python

• Broschiertes Buch

Produktbeschreibung

Fourth edition of a classic on computational physics with new chapters on data science, machine learning and general relativity, plus a plethora of supplementary materials.

Produktdetails

• Verlag: Wiley-VCH
• Artikelnr. des Verlages: 1141425 000
• 4. Aufl.
• Seitenzahl: 592
• Erscheinungstermin: 17. April 2024
• Englisch
• Abmessung: 244mm x 172mm x 33mm
• Gewicht: 1118g
• ISBN-13: 9783527414253
• ISBN-10: 3527414258
• Artikelnr.: 69226449

Autorenporträt

Rubin H. Landau is Professor Emeritus in the Department of Physics at Oregon State University in Corvallis. He has been teaching courses in computational physics for over 25 years, was a founder of the Computational Physics Degree Program and the Northwest Alliance for Computational Science and Engineering, and has been using computers in theoretical physics research ever since graduate school. He is author of more than 90 refereed publications and has also authored books on Quantum Mechanics, Workstations and Supercomputers, the first two editions of Computational Physics, and a First Course in Scientific Computing.   Manuel J. Páez is a professor in the Department of Physics at the University of Antioquia in Medellín, 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 taught Physics and Computer Science at the Military College "Stefan cel Mare" in Câmpulung Moldovenesc, Romania.

Inhaltsangabe

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

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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