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240104 ||| eng |
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|a 9783319024356
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|a A. Stickler, Benjamin
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| 245 |
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|a Basic Concepts in Computational Physics
|h Elektronische Ressource
|c by Benjamin A. Stickler, Ewald Schachinger
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| 250 |
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|a 1st ed. 2014
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2014, 2014
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| 300 |
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|a XVII, 377 p. 95 illus
|b online resource
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|a Some Basic Remarks -- Part I Deterministic Methods: Numerical Differentiation -- Numerical Integration -- The KEPLER Problem -- Ordinary Differential Equations – Initial Value Problems -- The Double Pendulum -- Molecular Dynamics -- Numerics of Ordinary Differential Equations - Boundary Value Problems -- The One-Dimensional Stationary Heat Equation -- The One-Dimensional Stationary SCHRÖDINGER Equation -- Numerics of Partial Differential Equations -- Part II Stochastic Methods -- Pseudo Random Number Generators -- Random Sampling Methods -- A Brief Introduction to Monte-Carlo Methods -- The ISING Model -- Some Basics of Stochastic Processes -- The Random Walk and Diffusion Theory -- MARKOV-Chain Monte Carlo and the POTTS Model -- Data Analysis -- Stochastic Optimization
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| 653 |
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|a Complex Systems
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| 653 |
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|a Chemistry, Physical and theoretical
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| 653 |
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|a Engineering mathematics
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| 653 |
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|a Theoretical Chemistry
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| 653 |
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|a Computational Mathematics and Numerical Analysis
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| 653 |
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|a Mathematics / Data processing
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| 653 |
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|a System theory
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| 653 |
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|a Mathematical physics
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| 653 |
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|a Engineering / Data processing
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| 653 |
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|a Theoretical, Mathematical and Computational Physics
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| 653 |
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|a Mathematical and Computational Engineering Applications
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| 700 |
1 |
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|a Schachinger, Ewald
|e [author]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
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|a 10.1007/978-3-319-02435-6
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| 856 |
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|u https://doi.org/10.1007/978-3-319-02435-6?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 530.1
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| 520 |
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|a With the development of ever more powerful computers a new branch of physics and engineering evolved over the last few decades: Computer Simulation or Computational Physics. It serves two main purposes: - Solution of complex mathematical problems such as, differential equations, minimization/optimization, or high-dimensional sums/integrals. - Direct simulation of physical processes, as for instance, molecular dynamics or Monte-Carlo simulation of physical/chemical/technical processes. Consequently, the book is divided into two main parts: Deterministic methods and stochastic methods. Based on concrete problems, the first part discusses numerical differentiation and integration, and the treatment of ordinary differential equations. This is augmented by notes on the numerics of partial differential equations. The second part discusses the generation of random numbers, summarizes the basics of stochastics which is then followed by the introduction of various Monte-Carlo (MC) methods. Specific emphasis is on MARKOV chain MC algorithms. All this is again augmented by numerous applications from physics. The final two chapters on Data Analysis and Stochastic Optimization share the two main topics as a common denominator. The book offers a number of appendices to provide the reader with more detailed information on various topics discussed in the main part. Nevertheless, the reader should be familiar with the most important concepts of statistics and probability theory albeit two appendices have been dedicated to provide a rudimentary discussion
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