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000760778 020__ $$a9783319272658$$q(electronic book)
000760778 020__ $$a3319272659$$q(electronic book)
000760778 020__ $$z9783319272634
000760778 0247_ $$a10.1007/978-3-319-27265-8$$2doi
000760778 035__ $$aSP(OCoLC)ocn945632196
000760778 035__ $$aSP(OCoLC)945632196
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000760778 050_4 $$aQC52
000760778 08204 $$a530.1$$223
000760778 1001_ $$aStickler, Benjamin A.,$$eauthor.
000760778 24510 $$aBasic concepts in computational physics /$$cBenjamin A. Stickler, Ewald Schachinger.
000760778 250__ $$aSecond edition.
000760778 264_1 $$aCham :$$bSpringer,$$c2016.
000760778 300__ $$a1 online resource (xvi, 409 pages) :$$billustrations
000760778 336__ $$atext$$btxt$$2rdacontent
000760778 337__ $$acomputer$$bc$$2rdamedia
000760778 338__ $$aonline resource$$bcr$$2rdacarrier
000760778 504__ $$aIncludes bibliographical references and index.
000760778 5050_ $$aSome Basic Remarks -- Part I Deterministic Methods -- Numerical Differentiation -- Numerical Integration -- The KEPLER Problem -- Ordinary Differential Equations -- Initial Value Problems -- The Double Pendulum -- Molecular Dynamics -- Numerics of Ordinary Differential Equations -- Boundary Value Problems -- The One-Dimensional Stationary Heat Equation -- The One-Dimensional Stationary SCHRÖDINGER Equation -- Partial Differential Equations -- Part II Stochastic Methods -- Pseudo Random Number Generators -- Random Sampling Methods -- A Brief Introduction to Monte-Carlo Methods -- The ISING Model -- Some Basics of Stochastic Processes -- The Random Walk and Diffusion Theory -- MARKOV-Chain Monte Carlo and the POTTS Model -- Data Analysis -- Stochastic Optimization -- Appendix: The Two-Body Problem -- Solving Non-Linear Equations. The NEWTON Method -- Numerical Solution of Systems of Equations -- Fast Fourier Transform -- Basics of Probability Theory -- Phase Transitions -- Fractional Integrals and Derivatives in 1D -- Least Squares Fit -- Deterministic Optimization.
000760778 506__ $$aAccess limited to authorized users.
000760778 520__ $$aThis new edition is a concise introduction to the basic methods of computational physics. Readers will discover the benefits of numerical methods for solving complex mathematical problems and for the direct simulation of physical processes. The book is divided into two main parts: Deterministic methods and stochastic methods in computational physics. Based on concrete problems, the first part discusses numerical differentiation and integration, as well as the treatment of ordinary differential equations. This is extended by a brief introduction to the numerics of partial differential equations. The second part deals with the generation of random numbers, summarizes the basics of stochastics, and subsequently introduces Monte-Carlo (MC) methods. Specific emphasis is on MARKOV chain MC algorithms. The final two chapters discuss data analysis and stochastic optimization. All this is again motivated and augmented by applications from physics. In addition, the book offers a number of appendices to provide the reader with information on topics not discussed in the main text. Numerous problems with worked-out solutions, chapter introductions and summaries, together with a clear and application-oriented style support the reader. Ready to use C++ codes are provided online.
000760778 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 28, 2016).
000760778 650_0 $$aPhysics$$xData processing.
000760778 650_0 $$aMathematical physics.
000760778 7001_ $$aSchachinger, E.,$$eauthor.
000760778 77608 $$iPrint version:$$z9783319272634
000760778 852__ $$bebk
000760778 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-27265-8$$zOnline Access$$91397441.1
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