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Computational Physics A Practical Introduction to Computational Physics and Scientific Computing 1st Edition by Konstantinos N Anagnostopoulos ISBN 1312318120 9781312318120

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Computational Physics A Practical Introduction to Computational Physics and Scientific Computing 1.0 Edition Konstantinos N. Anagnostopoulos Digital Instant Download

Author(s): Konstantinos N. Anagnostopoulos
ISBN(s): 9781312318120, 1312318120
Edition: 1.0
File Details: PDF, 20.97 MB
Year: 2014
Language: english
SKU: EB-4728000 Category: Tag:

Computational Physics A Practical Introduction to Computational Physics and Scientific Computing 1st Edition by Konstantinos N Anagnostopoulos – Ebook PDF Instant Download/Delivery: 1312318120, 9781312318120
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ISBN 10: 1312318120 
ISBN 13: 9781312318120
Author: Konstantinos N Anagnostopoulos

This book is an introduction to the computational methods used in physics and other related scientific fields. It is addressed to an audience that has already been exposed to the introductory level of college physics, usually taught during the first two years of an undergraduate program in science and engineering. It assumes no prior knowledge of numerical analysis, programming or computers and teaches whatever is necessary for the solution of the problems addressed in the text. C++ is used for programming the core programs and data analysis is performed using the powerful tools of the GNU/Linux environment. All the necessary software is open source and freely available. The book starts with very simple problems in particle motion and ends with an in-depth discussion of advanced techniques used in Monte Carlo simulations in statistical mechanics. The level of instruction rises slowly, while discussing problems like the diffusion equation, electrostatics on the plane, quantum mechanics and random walks.

Computational Physics A Practical Introduction to Computational Physics and Scientific Computing 1st Table of contents:

  • Foundations of Scientific Computing

    • Basics of Computational Tools and Software
    • Introduction to Programming Languages for Physics (Python, C++, MATLAB)
    • Numerical Precision and Error Analysis
    • Floating Point Arithmetic and Numerical Representation
    • Efficiency and Optimization of Computational Methods

  • Mathematical Concepts for Computational Physics

    • Linear Algebra for Physics Simulations
    • Differential Equations: Solvers and Approximations
    • Fourier Analysis and Discrete Transforms
    • Probability, Statistics, and Monte Carlo Methods
    • Numerical Integration and Differentiation

  • Solving Ordinary Differential Equations (ODEs)

    • Introduction to ODEs in Physical Systems
    • Euler’s Method and Higher-Order Approximations
    • Runge-Kutta Methods for Solving ODEs
    • Stability and Convergence of ODE Solvers
    • Application: Modeling Physical Systems Using ODEs

  • Partial Differential Equations (PDEs) and Their Numerical Solutions

    • Introduction to PDEs in Physics
    • Finite Difference Method for PDEs
    • Solving Heat Equation, Wave Equation, and Laplace Equation
    • Stability and Convergence of PDE Solvers
    • Using the Finite Element Method (FEM) and Other Advanced Techniques

  • Computational Techniques in Mechanics and Dynamics

    • Simulating Particle Motion and Systems of Particles
    • The Verlet Algorithm and Molecular Dynamics
    • Rigid Body Dynamics and Collisions
    • Simulating Fluids: Navier-Stokes Equations and Computational Fluid Dynamics (CFD)
    • Application: Simulating Physical Systems in Motion

  • Computational Methods in Quantum Mechanics

    • Introduction to Quantum Mechanics and Numerical Methods
    • Solving the Schrödinger Equation Using Numerical Approaches
    • Finite Difference Methods for Quantum Systems
    • Quantum Monte Carlo and Path Integral Methods
    • Application: Quantum Simulations in Condensed Matter Physics

  • Computational Electromagnetism

    • Introduction to Maxwell’s Equations and Their Numerical Solutions
    • The Finite-Difference Time-Domain (FDTD) Method
    • Electromagnetic Wave Propagation and Simulation
    • Application: Modeling Antennas and Waveguides
    • Computational Techniques in Optics and Photonics

  • Computational Fluid Dynamics (CFD)

    • Basics of Fluid Dynamics and Numerical Simulations
    • Navier-Stokes Equations and their Numerical Solutions
    • Grid Generation and Solution Methods
    • Turbulence Modeling and Flow Visualization
    • Application: Simulating Airflow Over Wings and Fluid Flows

  • Monte Carlo Simulations

    • Introduction to Random Processes and Stochastic Methods
    • Monte Carlo Method for Solving Physical Problems
    • Random Walks, Markov Chains, and Simulating Random Systems
    • Importance Sampling and Variance Reduction Techniques
    • Application: Simulating Particle Diffusion and Statistical Physics

  • Optimization Techniques for Computational Physics

    • Overview of Optimization Methods
    • Linear and Nonlinear Optimization
    • Gradient Descent and Newton’s Method
    • Simulated Annealing and Genetic Algorithms
    • Application: Optimizing Experimental Designs and Model Parameters

  • Parallel Computing and High-Performance Simulations

    • Introduction to Parallel Computing and Parallel Algorithms
    • MPI and OpenMP for Parallelism
    • GPU Computing and CUDA Programming
    • Scaling Algorithms for Large-Scale Simulations
    • Application: Simulations on Supercomputers and Clusters

  • Data Analysis and Visualization

    • Techniques for Data Processing and Statistical Analysis
    • Visualization Tools for Scientific Data
    • Plotting Libraries and Graphical User Interfaces (GUIs)
    • 3D Visualization in Computational Physics
    • Application: Analyzing Simulation Results and Experimental Data

  • Applications in Astrophysics and Cosmology

    • Computational Models of Stellar Evolution and Supernovae
    • Simulations of Cosmic Structures and Dark Matter
    • Numerical Relativity and Gravitational Wave Simulations
    • Simulating Black Hole Dynamics and Gravitational Interactions
    • Application: Modeling the Universe and Cosmological Phenomena

  • Applications in Condensed Matter Physics

    • Numerical Methods for Solid State Physics
    • Lattice Boltzmann Method and its Applications
    • Molecular Dynamics Simulations of Materials
    • Monte Carlo Methods in Statistical Mechanics
    • Application: Simulating Crystals, Liquids, and Magnetic Systems

  • Applications in Climate and Environmental Physics

    • Modeling Earth’s Climate System and Weather Patterns
    • Numerical Weather Prediction Models
    • Climate Change Simulations and Environmental Modeling
    • Computational Approaches to Oceanography and Ecology
    • Application: Studying Global Warming and Climate Trends

  • Introduction to Machine Learning in Computational Physics

    • Overview of Machine Learning Techniques in Physics
    • Neural Networks, Deep Learning, and Data-Driven Models
    • Data-Driven Discovery of Physical Laws
    • Applications in Quantum Mechanics and Fluid Dynamics
    • Future Directions for Machine Learning in Physics

  • Case Studies in Computational Physics

    • Case Study 1: Simulating Solar Flares and Space Weather
    • Case Study 2: Modeling Protein Folding and Biophysics
    • Case Study 3: Climate Simulation and Earth System Modeling
    • Case Study 4: Large-Scale Astrophysical Simulations
    • Case Study 5: Computational Fluid Dynamics in Engineering

  • Future Directions and Challenges in Computational Physics

    • The Role of Quantum Computing in Physics Simulations
    • The Integration of Artificial Intelligence in Scientific Computing
    • Challenges of Big Data and High-Performance Computing
    • Emerging Trends in Computational Physics and Simulation
    • Preparing for the Future of Computational Physics

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Konstantinos N Anagnostopoulos,Computational Physics,Scientific Computing