By Philipp O. J. Scherer

This textbook provides simple and complex computational physics in a really didactic variety. It includes very-well-presented and easy mathematical descriptions of some of the most crucial algorithms utilized in computational physics. Many transparent mathematical descriptions of vital recommendations in computational physics are given. the 1st a part of the ebook discusses the elemental numerical tools. a great number of routines and desktop experiments permits to review the homes of those tools. the second one half concentrates on simulation of classical and quantum platforms. It makes use of a slightly basic idea for the equation of movement that are utilized to dull and partial differential equations. numerous periods of integration equipment are mentioned together with not just the traditional Euler and Runge Kutta strategy but additionally multistep tools and the category of Verlet equipment that is brought via learning the movement in Liouville house. in addition to the classical equipment, inverse interpolation is mentioned, including the preferred mixed tools through Dekker and Brent and a now not so popular development through Chandrupatla. A common bankruptcy at the numerical remedy of differential equations offers tools of finite variations, finite volumes, finite components and boundary parts including spectral tools and weighted residual dependent equipment. A comparability of numerous tools for quantum platforms is played, containing pseudo-spectral equipment, finite adjustments tools, rational approximation to the time evolution operator, moment order differencing and break up operator methods.

The ebook provides basic yet non trivial examples from a wide variety of actual themes attempting to supply the reader perception into the numerical therapy but additionally the simulated difficulties. Rotational movement is handled in a lot element to explain the movement of inflexible rotors which might be only a basic spinning most sensible or a set of molecules or planets. The behaviour of straightforward quantum platforms is studied completely. One concentration is on a point process in an exterior box. resolution of the Bloch equations permits the simulation of a quantum bit and to appreciate easy rules from quantum optics. as an instance of a thermodynamic process, the Lennard Jones liquid is simulated. the foundations of molecular dynamics are proven with sensible simulations. A moment thermodynamic subject is the Ising version in a single and dimensions. the answer of the Poisson Boltzman equation is mentioned intimately that is extremely important in Biophysics in addition to in semiconductor physics. in addition to the normal finite point equipment, additionally sleek boundary aspect equipment are mentioned. Waves and diffusion procedures are simulated. diverse tools are in comparison in regards to their balance and potency. Random stroll types are studied with program to uncomplicated polymer physics. Nonlinear structures are mentioned intimately with program to inhabitants dynamics and response diffusion platforms. The workouts to the publication are learned as laptop experiments. a number of Java applets is supplied. it may be attempted out by way of the reader even with no programming talents. The reader can regulate the courses with the aid of the freely on hand and platform self sustaining programming surroundings "netbeans".

Show description

Read Online or Download Computational Physics: Simulation of Classical and Quantum Systems (2nd Edition) (Graduate Texts in Physics) PDF

Similar computer simulation books

Fundamentals of data structures

This article deals an entire rendering of uncomplicated info constitution implementations within the well known language C++.

Enhancing Embedded Systems Simulation: A Chip-Hardware-in-the-Loop Simulation Framework

Utilizing the genuine Microcontroller (µC) as a substitute for a µC version inside of a procedure simulation of a µC-based process is a giant profit because the µC is already validated. in addition, its greatest functionality and its accuracy are a lot better than any simulation version. With the Chip-Hardware-in-the-Loop Simulation (CHILS) process, Christian Köhler covers the relationship among µC and simulation, the research and optimization of such coupling structures in addition to the interface abstraction.

Convex Analysis and Global Optimization

This e-book offers state of the art effects and methodologies in smooth international optimization, and has been a staple reference for researchers, engineers, complex scholars (also in utilized mathematics), and practitioners in a variety of fields of engineering. the second one variation has been pointed out thus far and maintains to advance a coherent and rigorous conception of deterministic international optimization, highlighting the basic function of convex research.

Additional info for Computational Physics: Simulation of Classical and Quantum Systems (2nd Edition) (Graduate Texts in Physics)

Example text

63) Finally we calculate Mi from the continuity of s ′ (x). 66) which is a system of linear equations for the Mi . 67) hi hi + hi+1 yi+1 − yi yi − yi−1 − hi+1 hi µi = 1 − λi = di = we have 6 hi + hi+1 µi Mi−1 + 2Mi + λi Mi+1 = di i = 1 · · · n − 1. 71) and in case of given derivative values λ0 = 1 µ n = 1 d 0 = 6 y1 − y0 − y0′ h1 h1 dn = 6 yn − yn−1 y′ − . 4 Rational Interpolation 25 The system of equation has the form ⎡ ⎤ ⎡ ⎤ ⎤⎡ d0 M0 2 λ0 ⎢ µ 1 2 λ1 ⎥ ⎢ M1 ⎥ ⎢ d 1 ⎥ ⎢ ⎥ ⎢ ⎥ ⎥⎢ ⎢ µ2 2 λ2 ⎥ ⎢ M2 ⎥ ⎢ d 2 ⎥ ⎢ ⎥ ⎢ ⎥ ⎥⎢ ⎢ ⎥ ⎢ ..

Spline functions can be superior which are piecewise defined polynomials. Especially cubic splines are often used to draw smooth curves. Curves with poles can be represented by rational interpolating functions whereas a special class of rational interpolants without poles provides a rather new alternative to spline interpolation. 1 Interpolating Functions Consider the following problem: Given are n + 1 sample points (xi , fi ), i = 0 · · · n and a function of x which depends on n + 1 parameters ai : Φ(x; a0 · · · an ).

4) 37 38 3 Numerical Differentiation Fig. 1 (Numerical differentiation) Numerical differentiation approximates the differential quotient by a f difference quotient df dx ≈ x . However, approximation by a simple forward difference df f (x0 +h)−f (x0 ) , is dx (x0 ) ≈ h not very accurate f + f ε3 + f (x + h)ε1 − f (x)ε2 (1 + ε5 ) h(1 + ε4 ) f (x + h) f (1 + ε5 − ε4 + ε3 ) + ε1 = h h f (x) ε2 . 5) h The errors are uncorrelated and the relative error of the result can be estimated by f l÷ f , h(1 + ε4 ) = | f x − f x f x| ≤ 3εM + f (x) f x 2 εM .

Download PDF sample

Download Computational Physics: Simulation of Classical and Quantum by Philipp O. J. Scherer PDF
Rated 4.18 of 5 – based on 23 votes