Computation in modern physics:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New Jersey u.a.
World Scientific
2006
|
| Edition: | 3. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XII, 366 S. |
| ISBN: | 9789812567994 |
Staff View
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| 245 | 1 | 0 | |a Computation in modern physics |c William R. Gibbs |
| 250 | |a 3. ed. | ||
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Record in the Search Index
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| adam_text | Contents
List of Figures ix
Preface xi
1. Integration 1
1.1 Classical Quadrature 1
1.2 Orthogonal Polynomials 10
1.2.1 Orthogonal Polynomials in the Interval — 1 x 1 . . . . 10
1.2.2 General Orthogonal Polynomials 13
1.3 Gaussian Integration 14
1.3.1 Gauss Legendre Integration 16
1.3.2 Gauss Laguerre Integration 16
1.4 Special Integration Schemes 19
1.5 Principal Value Integrals 20
2. Introduction to Monte Carlo 27
2.1 Preliminary Notions Calculating tt 27
2.2 Evaluation of Integrals by Monte Carlo 29
2.3 Techniques for Direct Sampling 32
2.3.1 Cumulative Probability Distributions 33
2.3.2 The Characteristic Function j (t) 33
2.3.3 The Fundamental Theorem of Sampling 34
2.3.4 Sampling Monomials 0 a; 1 35
2.3.5 Sampling Functions 0 z oo 37
2.3.5.1 The Exponential Function 37
2.3.5.2 Other Algebraically Invertible Functions 37
2.3.5.3 Sampling a Gaussian Distribution 40
2.3.6 Brute force Inversion of F(x) 41
2.3.7 The Rejection Technique 42
2.3.8 Sums of Random Variables 43
2.3.9 Selection on the Random Variables 44
2.3.10 The Sum of Probability Distribution Functions 47
2.3.10.1 Special Cases 49
2.4 The Metropolis Algorithm 50
2.4.1 The Method Itself 50
2.4.2 Why It Works 53
V
vi Contents
2.4.3 Comments on the Algorithm 54
3. Differential Methods 61
3.1 Difference Schemes 61
3.1.1 Elementary Considerations 61
3.1.2 The General Case 62
3.2 Simple Differential Equations 64
3.3 Modeling with Differential Equations 68
4. Computers for Physicists 75
4.1 Fundamentals 76
4.1.1 Representation of Negative Numbers 77
4.1.2 Logical Operations 79
4.1.3 Integer Formats 80
4.1.3.1 Fixed Point Lengths 80
4.1.4 Floating Point Formats 81
4.1.5 Some Practical Conclusions 83
4.2 The i80X86 Series 84
4.2.1 The Stack 84
4.2.2 Memory Addressing 85
4.2.3 Internal Registers of the CPU 86
4.2.4 Instructions 87
4.2.5 A Sample Program 92
4.2.6 The Floating Point Co processor i8087 94
4.2.7 Two Important Bottlenecks 95
4.3 Cray 1 S Architecture 95
4.3.1 Vector Operations and Chaining 96
4.3.2 Coding for Maximum Speed 97
4.4 Intel i860 Architecture 98
4.5 Multi Processor Computer Systems 102
4.5.1 Amdahl s Law 102
4.5.2 Difficulties 103
4.5.3 One Practical Solution: Beowulf Clusters 103
4.5.4 Algorithm types 105
4.5.4.1 100% Algorithms 105
4.5.4.2 Semi efficient Algorithms 106
4.5.4.3 Costly algorithms 106
4.6 A Parallel Recursive Algorithm 108
5. Linear Algebra 115
5.1 x2 Analysis 115
5.2 Solution of Linear Equations 117
5.2.1 Gaussian Elimination 117
5.2.2 LU Reductions 120
5.2.3 Crout s LU Reduction 122
5.2.4 The Gauss Seidel Method 125
5.2.5 The Householder Transformation 127
5.3 The Eigenvalue Problem 130
5.3.1 Coupled Oscillators 130
5.3.2 Basic Properties 131
5.3.2.1 The Power Method for Finding Eigenvalues ... 132
5.3.2.2 The Inverse Power Method 133
Contents vii
5.3.3 Tridiagonal Symmetric Matrices 134
5.3.4 The Role of Orthogonal Matrices 138
5.3.5 The Householder Method for Eigenvalues 139
5.3.6 The Lanczos Algorithm 139
6. Exercises in Monte Carlo 147
6.1 The Potential Energy of the Oxygen Atom 147
6.2 Oxygen Potential Energy with Metropolis 151
6.3 Radiation Transport 153
6.4 An Inverse Problem with Monte Carlo 157
7. Finite Element Methods 161
7.1 Basis Functions One Dimension 162
7.2 Establishing the System Matrix 164
7.2.1 Model Problem 165
7.2.2 The Classical Procedure 165
7.2.3 The Galerkin Method 166
7.2.4 The Variational Method 168
7.3 Example One dimensional Program 169
7.4 Assembly by Elements 171
7.5 Problems in Two Dimensions 172
7.5.1 Element Functions 172
7.5.2 Laplace s Equation 174
8. Digital Signal Processing 181
8.1 Fundamental Concepts 181
8.2 Sampling: Nyquist Theorem 181
8.3 The Fast Fourier Transform 184
8.4 Phase Problems 189
9. Chaos 193
9.1 Functional Iteration 193
9.2 Finding the Critical Values 202
10. The Schrodinger Equation 209
10.1 Removal of the Time Dependence 209
10.2 Reduction of the Two body System 210
10.3 Expansion in Partial Waves 211
10.4 The Scattering Problem 213
10.4.1 The Scattering Amplitude 215
10.4.2 Model Nucleon nucleon Potentials 223
10.4.3 The Off shell Amplitude 225
10.4.4 A Relativistic Generalization 231
10.4.5 Formal Scattering Theory 232
10.4.6 Modeling the t matrix 233
10.4.7 Solutions with Exponential Potentials 235
10.4.8 Matching with Coulomb Waves 239
10.5 Bound States of the Schrodinger Equation 242
10.5.1 Nuclear Systems 244
10.5.2 Physics of Bound States: The Shell Model 244
10.5.3 Hypernuclei 247
viii Contents
10.5.4 The Deuteron 248
10.5.5 The One Pion Exchange Potential 251
10.6 Properties of the Clebsch Gordan Coefficients 256
10.7 Time Dependent Schrodinger Equation 258
11. The N body Ground State 269
11.1 The Variational Principle 270
11.1.1 A Sample Variational Problem 271
11.1.2 Variational Ground State of the4 He Nucleus 274
11.1.3 Variational Liquid 4He 278
11.2 Monte Carlo Green s Function Methods 281
11.2.1 The Green s Function Approach 282
11.2.2 Choosing Walkers for MCGF 289
11.3 Alternate Energy Estimators 290
11.3.1 Importance Sampling 292
11.3.2 An Example Algorithm 294
11.4 Scattering in the N body System 295
11.5 More General Methods 299
12. Divergent Series 303
12.1 Some Classic Examples 303
12.2 Generalizations of Cesaro Summation 305
12.3 Borel Summation 307
12.3.1 Borel s Differential Form 307
12.3.2 Borel s Integral Form 309
12.4 Pade Approximants 311
13. Scattering in the N body System 319
13.1 Single Scattering 319
13.2 First Order Optical Potential 322
13.2.1 Calculating the Non local Potential 324
13.2.2 Solving with a Non local Potential 328
13.3 Double Scattering 331
13.3.1 Relation of Double Scattering to Coherence 335
13.4 Scattering from Fixed Centers 336
13.5 The Watson Multiple Scattering Series 340
13.6 The KMT Optical Model 346
13.7 Medium Corrections 346
Appendix A Programs 355
A.I Legendre Polynomials 355
A.2 Gaussian Integration 356
A.3 Spherical Bessel Functions 359
A.4 Random Number Generator 362
Index 363
|
| adam_txt |
Contents
List of Figures ix
Preface xi
1. Integration 1
1.1 Classical Quadrature 1
1.2 Orthogonal Polynomials 10
1.2.1 Orthogonal Polynomials in the Interval — 1 x 1 . . . . 10
1.2.2 General Orthogonal Polynomials 13
1.3 Gaussian Integration 14
1.3.1 Gauss Legendre Integration 16
1.3.2 Gauss Laguerre Integration 16
1.4 Special Integration Schemes 19
1.5 Principal Value Integrals 20
2. Introduction to Monte Carlo 27
2.1 Preliminary Notions Calculating tt 27
2.2 Evaluation of Integrals by Monte Carlo 29
2.3 Techniques for Direct Sampling 32
2.3.1 Cumulative Probability Distributions 33
2.3.2 The Characteristic Function j (t) 33
2.3.3 The Fundamental Theorem of Sampling 34
2.3.4 Sampling Monomials 0 a; 1 35
2.3.5 Sampling Functions 0 z oo 37
2.3.5.1 The Exponential Function 37
2.3.5.2 Other Algebraically Invertible Functions 37
2.3.5.3 Sampling a Gaussian Distribution 40
2.3.6 Brute force Inversion of F(x) 41
2.3.7 The Rejection Technique 42
2.3.8 Sums of Random Variables 43
2.3.9 Selection on the Random Variables 44
2.3.10 The Sum of Probability Distribution Functions 47
2.3.10.1 Special Cases 49
2.4 The Metropolis Algorithm 50
2.4.1 The Method Itself 50
2.4.2 Why It Works 53
V
vi Contents
2.4.3 Comments on the Algorithm 54
3. Differential Methods 61
3.1 Difference Schemes 61
3.1.1 Elementary Considerations 61
3.1.2 The General Case 62
3.2 Simple Differential Equations 64
3.3 Modeling with Differential Equations 68
4. Computers for Physicists 75
4.1 Fundamentals 76
4.1.1 Representation of Negative Numbers 77
4.1.2 Logical Operations 79
4.1.3 Integer Formats 80
4.1.3.1 Fixed Point Lengths 80
4.1.4 Floating Point Formats 81
4.1.5 Some Practical Conclusions 83
4.2 The i80X86 Series 84
4.2.1 The Stack 84
4.2.2 Memory Addressing 85
4.2.3 Internal Registers of the CPU 86
4.2.4 Instructions 87
4.2.5 A Sample Program 92
4.2.6 The Floating Point Co processor i8087 94
4.2.7 Two Important Bottlenecks 95
4.3 Cray 1 S Architecture 95
4.3.1 Vector Operations and Chaining 96
4.3.2 Coding for Maximum Speed 97
4.4 Intel i860 Architecture 98
4.5 Multi Processor Computer Systems 102
4.5.1 Amdahl's Law 102
4.5.2 Difficulties 103
4.5.3 One Practical Solution: Beowulf Clusters 103
4.5.4 Algorithm types 105
4.5.4.1 "100%" Algorithms 105
4.5.4.2 Semi efficient Algorithms 106
4.5.4.3 Costly algorithms 106
4.6 A Parallel Recursive Algorithm 108
5. Linear Algebra 115
5.1 x2 Analysis 115
5.2 Solution of Linear Equations 117
5.2.1 Gaussian Elimination 117
5.2.2 LU Reductions 120
5.2.3 Crout's LU Reduction 122
5.2.4 The Gauss Seidel Method 125
5.2.5 The Householder Transformation 127
5.3 The Eigenvalue Problem 130
5.3.1 Coupled Oscillators 130
5.3.2 Basic Properties 131
5.3.2.1 The Power Method for Finding Eigenvalues . 132
5.3.2.2 The Inverse Power Method 133
Contents vii
5.3.3 Tridiagonal Symmetric Matrices 134
5.3.4 The Role of Orthogonal Matrices 138
5.3.5 The Householder Method for Eigenvalues 139
5.3.6 The Lanczos Algorithm 139
6. Exercises in Monte Carlo 147
6.1 The Potential Energy of the Oxygen Atom 147
6.2 Oxygen Potential Energy with Metropolis 151
6.3 Radiation Transport 153
6.4 An Inverse Problem with Monte Carlo 157
7. Finite Element Methods 161
7.1 Basis Functions One Dimension 162
7.2 Establishing the System Matrix 164
7.2.1 Model Problem 165
7.2.2 The "Classical" Procedure 165
7.2.3 The Galerkin Method 166
7.2.4 The Variational Method 168
7.3 Example One dimensional Program 169
7.4 Assembly by Elements 171
7.5 Problems in Two Dimensions 172
7.5.1 Element Functions 172
7.5.2 Laplace's Equation 174
8. Digital Signal Processing 181
8.1 Fundamental Concepts 181
8.2 Sampling: Nyquist Theorem 181
8.3 The Fast Fourier Transform 184
8.4 Phase Problems 189
9. Chaos 193
9.1 Functional Iteration 193
9.2 Finding the Critical Values 202
10. The Schrodinger Equation 209
10.1 Removal of the Time Dependence 209
10.2 Reduction of the Two body System 210
10.3 Expansion in Partial Waves 211
10.4 The Scattering Problem 213
10.4.1 The Scattering Amplitude 215
10.4.2 Model Nucleon nucleon Potentials 223
10.4.3 The Off shell Amplitude 225
10.4.4 A Relativistic Generalization 231
10.4.5 Formal Scattering Theory 232
10.4.6 Modeling the t matrix 233
10.4.7 Solutions with Exponential Potentials 235
10.4.8 Matching with Coulomb Waves 239
10.5 Bound States of the Schrodinger Equation 242
10.5.1 Nuclear Systems 244
10.5.2 Physics of Bound States: The Shell Model 244
10.5.3 Hypernuclei 247
viii Contents
10.5.4 The Deuteron 248
10.5.5 The One Pion Exchange Potential 251
10.6 Properties of the Clebsch Gordan Coefficients 256
10.7 Time Dependent Schrodinger Equation 258
11. The N body Ground State 269
11.1 The Variational Principle 270
11.1.1 A Sample Variational Problem 271
11.1.2 Variational Ground State of the4 He Nucleus 274
11.1.3 Variational Liquid 4He 278
11.2 Monte Carlo Green's Function Methods 281
11.2.1 The Green's Function Approach 282
11.2.2 Choosing Walkers for MCGF 289
11.3 Alternate Energy Estimators 290
11.3.1 Importance Sampling 292
11.3.2 An Example Algorithm 294
11.4 Scattering in the N body System 295
11.5 More General Methods 299
12. Divergent Series 303
12.1 Some Classic Examples 303
12.2 Generalizations of Cesaro Summation 305
12.3 Borel Summation 307
12.3.1 Borel's Differential Form 307
12.3.2 Borel's Integral Form 309
12.4 Pade Approximants 311
13. Scattering in the N body System 319
13.1 Single Scattering 319
13.2 First Order Optical Potential 322
13.2.1 Calculating the Non local Potential 324
13.2.2 Solving with a Non local Potential 328
13.3 Double Scattering 331
13.3.1 Relation of Double Scattering to Coherence 335
13.4 Scattering from Fixed Centers 336
13.5 The Watson Multiple Scattering Series 340
13.6 The KMT Optical Model 346
13.7 Medium Corrections 346
Appendix A Programs 355
A.I Legendre Polynomials 355
A.2 Gaussian Integration 356
A.3 Spherical Bessel Functions 359
A.4 Random Number Generator 362
Index 363 |
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| illustrated | Not Illustrated |
| index_date | 2024-07-02T15:25:19Z |
| indexdate | 2024-07-09T20:42:39Z |
| institution | BVB |
| isbn | 9789812567994 |
| language | English |
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| physical | XII, 366 S. |
| publishDate | 2006 |
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| spelling | Gibbs, William R. Verfasser aut Computation in modern physics William R. Gibbs 3. ed. New Jersey u.a. World Scientific 2006 XII, 366 S. txt rdacontent n rdamedia nc rdacarrier Physique mathématique Théorie quantique Mathematische Physik Quantentheorie Mathematical physics Quantum theory Computerphysik (DE-588)4273564-6 gnd rswk-swf Computerphysik (DE-588)4273564-6 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014941227&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gibbs, William R. Computation in modern physics Physique mathématique Théorie quantique Mathematische Physik Quantentheorie Mathematical physics Quantum theory Computerphysik (DE-588)4273564-6 gnd |
| subject_GND | (DE-588)4273564-6 |
| title | Computation in modern physics |
| title_auth | Computation in modern physics |
| title_exact_search | Computation in modern physics |
| title_exact_search_txtP | Computation in modern physics |
| title_full | Computation in modern physics William R. Gibbs |
| title_fullStr | Computation in modern physics William R. Gibbs |
| title_full_unstemmed | Computation in modern physics William R. Gibbs |
| title_short | Computation in modern physics |
| title_sort | computation in modern physics |
| topic | Physique mathématique Théorie quantique Mathematische Physik Quantentheorie Mathematical physics Quantum theory Computerphysik (DE-588)4273564-6 gnd |
| topic_facet | Physique mathématique Théorie quantique Mathematische Physik Quantentheorie Mathematical physics Quantum theory Computerphysik |
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