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E-raamat: Computational Physics: Simulation of Classical and Quantum Systems

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  • Sari: Graduate Texts in Physics
  • Ilmumisaeg: 07-Sep-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319610887
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  • Formaat: EPUB+DRM
  • Sari: Graduate Texts in Physics
  • Ilmumisaeg: 07-Sep-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319610887

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This book presents basic and advanced computational physics. It contains well-presented and simple mathematical descriptions of key algorithms. Includes exercises and solutions to all topics, as well as computer experiments.

This textbook presents basic numerical methods and applies them to a large variety of physical models in multiple computer experiments. Classical algorithms and more recent methods are explained. Partial differential equations are treated generally comparing important methods, and equations of motion are solved by a large number of simple as well as more sophisticated methods. Several modern algorithms for quantum wavepacket motion are compared. The first part of the book discusses the basic numerical methods, while the second part simulates classical and quantum systems. Simple but non-trivial examples from a broad range of physical topics offer readers insights into the numerical treatment but also the simulated problems. Rotational motion is studied in detail, as are simple quantum systems. A two-level system in an external field demonstrates elementary principles from quantum optics and simulation of a quantum bit. Principles of molecular dynamics are shown. Modern boundary element methods are presented in addition to standard methods, and waves and diffusion processes are simulated comparing the stability and efficiency of different methods. A large number of computer experiments is provided, which can be tried out even by readers with no programming skills. Exercises in the applets complete the pedagogical treatment in the book. In the third edition Monte Carlo methods and random number generation have been updated taking recent developments into account. Krylov-space methods for eigenvalue problems are discussed in much more detail. The wavelet transformation method has been included as well as simple applications to continuum mechanics and convection-diffusion problems. Lastly, elementary quantum many-body problems demonstrate the application of variational and Monte-Carlo methods.

Part I Numerical Methods
1 Error Analysis
3(14)
1.1 Machine Numbers and Rounding Errors
3(4)
1.2 Numerical Errors of Elementary Floating Point Operations
7(3)
1.2.1 Numerical Extinction
7(1)
1.2.2 Addition
8(1)
1.2.3 Multiplication
9(1)
1.3 Error Propagation
10(2)
1.4 Stability of Iterative Algorithms
12(1)
1.5 Example: Rotation
13(1)
1.6 Truncation Error
14(3)
Problems
15(2)
2 Interpolation
17(22)
2.1 Interpolating Functions
17(2)
2.2 Polynomial Interpolation
19(5)
2.2.1 Lagrange Polynomials
19(1)
2.2.2 Barycentric Lagrange Interpolation
19(2)
2.2.3 Newton's Divided Differences
21(1)
2.2.4 Neville Method
22(1)
2.2.5 Error of Polynomial Interpolation
23(1)
2.3 Spline Interpolation
24(4)
2.4 Rational Interpolation
28(7)
2.4.1 Pade Approximant
29(1)
2.4.2 Barycentric Rational Interpolation
30(5)
2.5 Multivariate Interpolation
35(4)
Problems
37(2)
3 Numerical Differentiation
39(8)
3.1 One-Sided Difference Quotient
39(2)
3.2 Central Difference Quotient
41(1)
3.3 Extrapolation Methods
41(3)
3.4 Higher Derivatives
44(1)
3.5 Partial Derivatives of Multivariate Functions
45(2)
Problems
46(1)
4 Numerical Integration
47(16)
4.1 Equidistant Sample Points
48(5)
4.1.1 Closed Newton--Cotes Formulae
49(1)
4.1.2 Open Newton--Cotes Formulae
50(1)
4.1.3 Composite Newton--Cotes Rules
50(1)
4.1.4 Extrapolation Method (Romberg Integration)
51(2)
4.2 Optimized Sample Points
53(10)
4.2.1 Clenshaw--Curtis Expressions
53(3)
4.2.2 Gaussian Integration
56(5)
Problems
61(2)
5 Systems of Inhomogeneous Linear Equations
63(34)
5.1 Gaussian Elimination Method
64(5)
5.1.1 Pivoting
68(1)
5.1.2 Direct LU Decomposition
68(1)
5.2 QR Decomposition
69(5)
5.2.1 QR Decomposition by Orthogonalization
69(2)
5.2.2 QR Decomposition by Householder Reflections
71(3)
5.3 Linear Equations with Tridiagonal Matrix
74(3)
5.4 Cyclic Tridiagonal Systems
77(1)
5.5 Linear Stationary Iteration
78(5)
5.5.1 Richardson-Iteration
79(1)
5.5.2 Matrix Splitting Methods
80(1)
5.5.3 Jacobi Method
80(1)
5.5.4 Gauss-Seidel Method
81(1)
5.5.5 Damping and Successive Over-relaxation
81(2)
5.6 Non Stationary Iterative Methods
83(9)
5.6.1 Krylov Space Methods
83(1)
5.6.2 Minimization Principle for Symmetric Positive Definite Systems
84(1)
5.6.3 Gradient Method
85(1)
5.6.4 Conjugate Gradients Method
86(3)
5.6.5 Non Symmetric Systems
89(3)
5.7 Matrix Inversion
92(5)
Problem
93(4)
6 Roots and Extremal Points
97(32)
6.1 Root Finding
98(16)
6.1.1 Bisection
98(1)
6.1.2 Regula Falsi (False Position) Method
99(1)
6.1.3 Newton--Raphson Method
100(1)
6.1.4 Secant Method
101(1)
6.1.5 Interpolation
101(1)
6.1.6 Inverse Interpolation
102(3)
6.1.7 Combined Methods
105(6)
6.1.8 Multidimensional Root Finding
111(2)
6.1.9 Quasi-Newton Methods
113(1)
6.2 Function Minimization
114(15)
6.2.1 The Ternary Search Method
115(1)
6.2.2 The Golden Section Search Method (Brent's Method)
116(5)
6.2.3 Minimization in Multidimensions
121(1)
6.2.4 Steepest Descent Method
122(2)
6.2.5 Conjugate Gradient Method
124(1)
6.2.6 Newton--Raphson Method
124(1)
6.2.7 Quasi-Newton Methods
125(1)
Problems
126(3)
7 Fourier Transformation
129(16)
7.1 Fourier Integral and Fourier Series
129(1)
7.2 Discrete Fourier Transformation
130(6)
7.2.1 Trigonometric Interpolation
132(2)
7.2.2 Real Valued Functions
134(1)
7.2.3 Approximate Continuous Fourier Transformation
135(1)
7.3 Fourier Transform Algorithms
136(9)
7.3.1 Goertzel's Algorithm
136(2)
7.3.2 Fast Fourier Transformation
138(3)
Problems
141(4)
8 Time-Frequency Analysis
145(42)
8.1 Short Time Fourier Transform (STFT)
145(7)
8.2 Discrete Short Time Fourier Transform
152(4)
8.3 Gabor Expansion
156(2)
8.4 Wavelet Analysis
158(2)
8.5 Wavelet Synthesis
160(4)
8.6 Discrete Wavelet Transform and Multiresolution Analysis
164(14)
8.6.1 Scaling Function and Multiresolution Approximation
164(7)
8.6.2 Construction of an Orthonormal Wavelet Basis
171(7)
8.7 Discrete Data and Fast Wavelet Transform
178(9)
8.7.1 Recursive Wavelet Transformation
178(2)
8.7.2 Example: Haar Wavelet
180(1)
8.7.3 Signal Reconstruction
181(1)
8.7.4 Example: Analysis with Compactly Supported Wavelets
182(2)
Problems
184(3)
9 Random Numbers and Monte-Carlo Methods
187(26)
9.1 Some Basic Statistics
187(9)
9.1.1 Probability Density and Cumulative Probability Distribution
187(1)
9.1.2 Histogram
188(1)
9.1.3 Expectation Values and Moments
189(1)
9.1.4 Example: Fair Die
190(1)
9.1.5 Normal Distribution
191(1)
9.1.6 Multivariate Distributions
192(1)
9.1.7 Central Limit Theorem
193(1)
9.1.8 Example: Binomial Distribution
194(1)
9.1.9 Average of Repeated Measurements
195(1)
9.2 Random Numbers
196(6)
9.2.1 Linear Congruent Mapping (LC)
197(1)
9.2.2 Xorshift
197(1)
9.2.3 Multiply with Carry (MWC)
198(1)
9.2.4 Complementary Multiply with Carry (CMWC)
199(1)
9.2.5 Random Numbers with Given Distribution
199(1)
9.2.6 Examples
200(2)
9.3 Monte-Carlo Integration
202(11)
9.3.1 Numerical Calculation of π
202(1)
9.3.2 Calculation of an Integral
202(2)
9.3.3 More General Random Numbers
204(1)
9.3.4 Configuration Integrals
204(2)
9.3.5 Simple Sampling
206(1)
9.3.6 Importance Sampling
207(1)
9.3.7 Metropolis Algorithm
207(3)
Problems
210(3)
10 Eigenvalue Problems
213(22)
10.1 Direct Solution
214(1)
10.2 Jacobi Method
214(3)
10.3 Tridiagonal Matrices
217(6)
10.3.1 Characteristic Polynomial of a Tridiagonal Matrix
217(1)
10.3.2 Special Tridiagonal Matrices
218(5)
10.4 Reduction to a Tridiagonal Matrix
223(2)
10.5 The Power Iteration Method
225(3)
10.6 The QR Algorithm
228(2)
10.7 Hermitian Matrices
230(1)
10.8 Large Matrices
231(3)
10.9 Non-symmetric Matrices
234(1)
Problems
234(1)
11 Data Fitting
235(20)
11.1 Least Square Fit
236(6)
11.1.1 Linear Least Square Fit
237(2)
11.1.2 Linear Least Square Fit with Orthogonalization
239(3)
11.2 Singular Value Decomposition
242(13)
11.2.1 Full Singular Value Decomposition
243(1)
11.2.2 Reduced Singular Value Decomposition
243(2)
11.2.3 Low Rank Matrix Approximation
245(3)
11.2.4 Linear Least Square Fit with Singular Value Decomposition
248(3)
11.2.5 Singular and Underdetermined Linear Systems of Equations
251(2)
Problems
253(2)
12 Discretization of Differential Equations
255(34)
12.1 Classification of Differential Equations
256(3)
12.2 Finite Differences
259(6)
12.2.1 Finite Differences in Time
259(1)
12.2.2 Stability Analysis
260(1)
12.2.3 Method of Lines
261(1)
12.2.4 Eigenvector Expansion
262(3)
12.3 Finite Volumes
265(5)
12.3.1 Discretization of fluxes
268(2)
12.4 Weighted Residual Based Methods
270(3)
12.4.1 Point Collocation Method
271(1)
12.4.2 Sub-domain Method
271(1)
12.4.3 Least Squares Method
272(1)
12.4.4 Galerkin Method
273(1)
12.5 Spectral and Pseudo-Spectral Methods
273(4)
12.5.1 Fourier Pseudo-Spectral Methods
273(1)
12.5.2 Example: Polynomial Approximation
274(3)
12.6 Finite Elements
277(9)
12.6.1 One-Dimensional Elements
277(1)
12.6.2 Two-and Three-Dimensional Elements
278(4)
12.6.3 One-Dimensional Galerkin FEM
282(4)
12.7 Boundary Element Method
286(3)
13 Equations of Motion
289(36)
13.1 The State Vector
290(1)
13.2 Time Evolution of the State Vector
291(1)
13.3 Explicit Forward Euler Method
292(3)
13.4 Implicit Backward Euler Method
295(1)
13.5 Improved Euler Methods
296(2)
13.6 Taylor Series Methods
298(3)
13.6.1 Nordsieck Predictor-Corrector Method
298(2)
13.6.2 Gear Predictor-Corrector Methods
300(1)
13.7 Runge--Kutta Methods
301(3)
13.7.1 Second Order Runge--Kutta Method
302(1)
13.7.2 Third Order Runge--Kutta Method
302(1)
13.7.3 Fourth Order Runge--Kutta Method
303(1)
13.8 Quality Control and Adaptive Step Size Control
304(1)
13.9 Extrapolation Methods
305(1)
13.10 Linear Multistep Methods
306(4)
13.10.1 Adams--Bashforth Methods
306(1)
13.10.2 Adams--Moulton Methods
307(1)
13.10.3 Backward Differentiation (Gear) Methods
308(1)
13.10.4 Predictor-Corrector Methods
309(1)
13.11 Verlet Methods
310(15)
13.11.1 Liouville Equation
310(1)
13.11.2 Split Operator Approximation
311(1)
13.11.3 Position Verlet Method
312(1)
13.11.4 Velocity Verlet Method
313(1)
13.11.5 Stoermer-Verlet Method
313(2)
13.11.6 Error Accumulation for the Stoermer-Verlet Method
315(1)
13.11.7 Beeman's Method
315(2)
13.11.8 The Leapfrog Method
317(1)
Problems
318(7)
Part II Simulation of Classical and Quantum Systems
14 Rotational Motion
325(26)
14.1 Transformation to a Body Fixed Coordinate System
325(1)
14.2 Properties of the Rotation Matrix
326(2)
14.3 Properties of W, Connection with the Vector of Angular Velocity
328(2)
14.4 Transformation Properties of the Angular Velocity
330(2)
14.5 Momentum and Angular Momentum
332(1)
14.6 Equations of Motion of a Rigid Body
333(1)
14.7 Moments of Inertia
334(1)
14.8 Equations of Motion for a Rotor
334(1)
14.9 Explicit Methods
335(2)
14.10 Loss of Orthogonality
337(1)
14.11 Implicit Method
338(3)
14.12 Example: Free Symmetric Rotor
341(1)
14.13 Kinetic Energy of a Rotor
342(1)
14.14 Parametrization by Euler Angles
342(1)
14.15 Cayley--Klein-Parameters, Quaternions, Euler Parameters
343(3)
14.16 Solving the Equations of Motion with Quaternions
346(5)
Problems
347(4)
15 Molecular Mechanics
351(18)
15.1 Atomic Coordinates
352(3)
15.2 Force Fields
355(3)
15.2.1 Intramolecular Forces
355(2)
15.2.2 Intermolecular Interactions
357(1)
15.3 Gradients
358(6)
15.4 Normal Mode Analysis
364(5)
15.4.1 Harmonic Approximation
364(3)
Problems
367(2)
16 Thermodynamic Systems
369(16)
16.1 Simulation of a Lennard--Jones Fluid
370(8)
16.1.1 Integration of the Equations of Motion
370(1)
16.1.2 Boundary Conditions and Average Pressure
371(1)
16.1.3 Initial Conditions and Average Temperature
372(1)
16.1.4 Analysis of the Results
373(5)
16.2 Monte-Carlo Simulation
378(7)
16.2.1 One-Dimensional Ising Model
378(2)
16.2.2 Two-Dimensional Ising Model
380(1)
Problems
381(4)
17 Random Walk and Brownian Motion
385(14)
17.1 Markovian Discrete Time Models
385(1)
17.2 Random Walk in One Dimension
386(3)
17.2.1 Random Walk with Constant Step Size
387(2)
17.3 The Freely Jointed Chain
389(6)
17.3.1 Basic Statistic Properties
389(3)
17.3.2 Gyration Tensor
392(1)
17.3.3 Hookean Spring Model
393(2)
17.4 Langevin Dynamics
395(4)
Problems
397(2)
18 Electrostatics
399(28)
18.1 Poisson Equation
400(11)
18.1.1 Homogeneous Dielectric Medium
400(2)
18.1.2 Numerical Methods for the Poisson Equation
402(1)
18.1.3 Charged Sphere
403(3)
18.1.4 Variable ε
406(1)
18.1.5 Discontinuous ε
407(1)
18.1.6 Solvation Energy of a Charged Sphere
408(1)
18.1.7 The Shifted Grid Method
409(2)
18.2 Poisson-Boltzmann Equation
411(2)
18.2.1 Linearization of the Poisson-Boltzmann Equation
412(1)
18.2.2 Discretization of the Linearized Poisson Boltzmann Equation
413(1)
18.3 Boundary Element Method for the Poisson Equation
413(7)
18.3.1 Integral Equations for the Potential
414(2)
18.3.2 Calculation of the Boundary Potential
416(4)
18.4 Boundary Element Method for the Linearized Poisson-Boltzmann Equation
420(1)
18.5 Electrostatic Interaction Energy (Onsager Model)
421(6)
18.5.1 Example: Point Charge in a Spherical Cavity
422(1)
Problems
423(4)
19 Advection
427(28)
19.1 The Advection Equation
427(1)
19.2 Advection in One Dimension
428(23)
19.2.1 Spatial Discretization with Finite Differences
430(3)
19.2.2 Explicit Methods
433(10)
19.2.3 Implicit Methods
443(2)
19.2.4 Finite Volume Methods
445(4)
19.2.5 Taylor--Galerkin Methods
449(2)
19.3 Advection in More Dimensions
451(4)
19.3.1 Lax--Wendroff Type Methods
452(1)
19.3.2 Finite Volume Methods
452(2)
19.3.3 Dimensional Splitting
454(1)
Problems
454(1)
20 Waves
455(24)
20.1 Classical Waves
455(3)
20.2 Spatial Discretization in One Dimension
458(3)
20.3 Solution by an Eigenvector Expansion
461(2)
20.4 Discretization of Space and Time
463(1)
20.5 Numerical Integration with a Two-Step Method
464(3)
20.6 Reduction to a First Order Differential Equation
467(3)
20.7 Two Variable Method
470(9)
20.7.1 Leapfrog Scheme
471(1)
20.7.2 Lax--Wendroff Scheme
472(2)
20.7.3 Crank--Nicolson Scheme
474(3)
Problems
477(2)
21 Diffusion
479(14)
21.1 Particle Flux and Concentration Changes
479(2)
21.2 Diffusion in One Dimension
481(9)
21.2.1 Explicit Euler (Forward Time Centered Space) Scheme
483(2)
21.2.2 Implicit Euler (Backward Time Centered Space) Scheme
485(1)
21.2.3 Crank--Nicolson Method
486(2)
21.2.4 Error Order Analysis
488(1)
21.2.5 Finite Element Discretization
489(1)
21.3 Split-Operator Method for Multidimensions
490(3)
Problems
491(2)
22 Nonlinear Systems
493(24)
22.1 Iterated Functions
494(7)
22.1.1 Fixed Points and Stability
494(2)
22.1.2 The Ljapunov-Exponent
496(1)
22.1.3 The Logistic Map
497(1)
22.1.4 Fixed Points of the Logistic Map
498(2)
22.1.5 Bifurcation Diagram
500(1)
22.2 Population Dynamics
501(2)
22.2.1 Equilibria and Stability
501(1)
22.2.2 The Continuous Logistic Model
502(1)
22.3 Lotka-Volterra Model
503(2)
22.3.1 Stability Analysis
504(1)
22.4 Functional Response
505(4)
22.4.1 Holling-Tanner Model
506(3)
22.5 Reaction-Diffusion Systems
509(8)
22.5.1 General Properties of Reaction-Diffusion Systems
509(1)
22.5.2 Chemical Reactions
509(2)
22.5.3 Diffusive Population Dynamics
511(1)
22.5.4 Stability Analysis
511(2)
22.5.5 Lotka Volterra Model with Diffusion
513(1)
Problems
514(3)
23 Simple Quantum Systems
517(58)
23.1 Pure and Mixed Quantum States
518(4)
23.1.1 Wavefunctions
519(1)
23.1.2 Density Matrix for an Ensemble of Systems
520(1)
23.1.3 Time Evolution of the Density Matrix
520(2)
23.2 Wave Packet Motion in One Dimension
522(15)
23.2.1 Discretization of the Kinetic Energy
523(2)
23.2.2 Time Evolution
525(11)
23.2.3 Example: Free Wave Packet Motion
536(1)
23.3 Few-State Systems
537(18)
23.3.1 Two-State System
540(3)
23.3.2 Two-State System with Time Dependent Perturbation
543(2)
23.3.3 Superexchange Model
545(3)
23.3.4 Ladder Model for Exponential Decay
548(3)
23.3.5 Semiclassical Curve Crossing
551(2)
23.3.6 Landau-Zener Model
553(2)
23.4 The Dissipative Two-State System
555(20)
23.4.1 Equations of Motion for a Two-State System
555(1)
23.4.2 The Vector Model
556(2)
23.4.3 The Spin-1/2 System
558(1)
23.4.4 Relaxation Processes - The Bloch Equations
559(2)
23.4.5 The Driven Two-State System
561(8)
23.4.6 Elementary Qubit Manipulation
569(3)
Problems
572(3)
24 Variational Methods for Quantum Systems
575(30)
24.1 Variational Quantum Monte Carlo Simulation of Atomic and Molecular Systems
577(12)
24.1.1 The Simplest Molecule: H2+
579(3)
24.1.2 The Simplest Two-Electron System: The Helium Atom
582(4)
24.1.3 The Hydrogen Molecule H2
586(3)
24.2 Exciton-Phonon Coupling in Molecular Aggregates
589(16)
24.2.1 Molecular Dimer
592(6)
24.2.2 Larger Aggregates
598(3)
Problems
601(4)
Appendix A Performing the Computer Experiments 605(4)
Appendix B Methods and Algorithms 609(8)
References 617(10)
Index 627
Prof. Scherer received his PhD in experimental and theoretical physics in 1984. He habilitated in theoretical physics and has been a lecturer at the Technical University of Munich (TUM) since 1999. He joined the National Institute of Advanced Industrial Science and Technology (AIST) in Tsukuba, Japan, as a visiting scientist in 2001 and 2003. From 2006 to 2008 he has been temporary leader of the Institute for Theoretical Biomolecular Physics at TUM. Ever since he has been an adjunct professor at the physics faculty of TUM. His area of research includes biomolecular physics and the computer simulation of molecular systems with classical and quantum methods. He published books on theoretical molecular physics and computational physics.