000			04079nam a22004935i 4500
001			978-3-319-02435-6
003			DE-He213
005			20140220082511.0
007			cr nn 008mamaa
008			131210s2014 gw | s |||| 0|eng d
020			_a9783319024356 _9978-3-319-02435-6
024	7		_a10.1007/978-3-319-02435-6 _2doi
050		4	_aQC1-999
072		7	_aPHU _2bicssc
072		7	_aSCI040000 _2bisacsh
082	0	4	_a530.1 _223
100	1		_aA. Stickler, Benjamin. _eauthor.
245	1	0	_aBasic Concepts in Computational Physics _h[electronic resource] / _cby Benjamin A. Stickler, Ewald Schachinger.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014.
300			_aXVII, 377 p. 95 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aSome 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.
520			_aWith 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.
650		0	_aPhysics.
650		0	_aChemistry.
650		0	_aComputer science _xMathematics.
650		0	_aEngineering mathematics.
650	1	4	_aPhysics.
650	2	4	_aNumerical and Computational Physics.
650	2	4	_aAppl.Mathematics/Computational Methods of Engineering.
650	2	4	_aComputational Mathematics and Numerical Analysis.
650	2	4	_aTheoretical and Computational Chemistry.
650	2	4	_aStatistical Physics, Dynamical Systems and Complexity.
700	1		_aSchachinger, Ewald. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319024349
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-02435-6
912			_aZDB-2-PHA
999			_c92858 _d92858