A guide to Monte Carlo simulations in statistical physics:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2009
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| Ausgabe: | 3. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XV, 471 S. Ill., graph. Darst. |
| ISBN: | 9780521768481 9781107649804 |
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| 245 | 1 | 0 | |a A guide to Monte Carlo simulations in statistical physics |c David P. Landau ; Kurt Binder |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2009 | |
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Datensatz im Suchindex
| _version_ | 1804140845015760896 |
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| adam_text | A GUIDE TO MONTE CARLO SIMULATIONS IN STATISTICAL PHYSICS THIRD EDITION
DAVID P. LANDAU CENTER FOR SIMULATIONAL PHYSICS, THE UNIVERSITY OF
GEORGIA KURT BINDER INSTITUT FUER PHYSIK, JOHANNES-GUTENBERG-UNIVERSITAET
MAINZ * CAMBRIDGE ** UNIVERSITY PRESS CONTENTS PAGE PREFACE 1
INTRODUCTION 1 1.1 WHAT IS A MONTE CARLO SIMULATION? 1 1.2 WHAT PROBLEMS
CAN WE SOLVE WITH IT? 2 1.3 WHAT DIFFICULTIES WILL WE ENCOUNTER? 3 1.3.1
LIMITED COMPUTER TIME AND MEMORY 3 1.3.2 STATISTICAL AND OTHER ERRORS 3
1.4 WHAT STRATEGY SHOULD WE FOLLOW IN APPROACHING A PROBLEM? 4 1.5 HOW
DO SIMULATIONS RELATE TO THEORY AND EXPERIMENT? 4 1.6 PERSPECTIVE 6
REFERENCES 6 2 SOME NECESSARY BACKGROUND 7 2.1 THERMODYNAMICS AND
STATISTICAL MECHANICS: A QUICK REMINDER 7 2.1.1 BASIC NOTIONS 7 2.1.2
PHASE TRANSITIONS 13 2.1.3 ERGODICITY AND BROKEN SYMMETRY 25 2.1.4
FLUCTUATIONS AND THE GINZBURG CRITERION 26 2.1.5 A STANDARD EXERCISE:
THE FERROMAGNETIC ISING MODEL 27 2.2 PROBABILITY THEORY 28 2.2.1 BASIC
NOTIONS 28 2.2.2 SPECIAL PROBABILITY DISTRIBUTIONS AND THE CENTRAL LIMIT
THEOREM 30 2.2.3 STATISTICAL ERRORS 31 2.2.4 MARKOV CHAINS AND MASTER
EQUATIONS 32 2.2.5 THE ART OF RANDOM NUMBER GENERATION 34 2.3
NON-EQUILIBRIUM AND DYNAMICS: SOME INTRODUCTORY COMMENTS 39 2.3.1
PHYSICAL APPLICATIONS OF MASTER EQUATIONS 39 2.3.2 CONSERVATION LAWS AND
THEIR CONSEQUENCES 41 2.3.3 CRITICAL SLOWING DOWN AT PHASE TRANSITIONS
44 2.3.4 TRANSPORT COEFFICIENTS 45 2.3.5 CONCLUDING COMMENTS 46
REFERENCES 46 VI CONTENTS 3 SIMPLE SAMPLING MONTE CARLO METHODS 48 3.1
INTRODUCTION 48 3.2 COMPARISONS OF METHODS FOR NUMERICAL INTEGRATION OF
GIVEN FUNCTIONS 48 3.2.1 SIMPLE METHODS 48 3.2.2 INTELLIGENT METHODS SO
3.3 BOUNDARY VALUE PROBLEMS 51 3.4 SIMULATION OF RADIOACTIVE DECAY 53
3.5 SIMULATION OF TRANSPORT PROPERTIES 54 3.5.1 NEUTRON TRANSPORT 54
3.5.2 FLUID FLOW 55 3.6 THE PERCOLATION PROBLEM 56 3.6.1 SITE
PERCOLATION 56 3.6.2 CLUSTER COUNTING: THE HOSHEN-KOPELMAN ALGORITHM 59
3.6.3 OTHER PERCOLATION MODELS 60 3.7 FINDING THE GROUNDSTATE OF A
HAMILTONIAN 60 3.8 GENERATION OF RANDOM WALKS 61 3.8.1 INTRODUCTION 61
3.8.2 RANDOM WALKS 62 3.8.3 SELF-AVOIDING WALKS 63 3.8.4 GROWING WALKS
AND OTHER MODELS 65 3.9 FINAL REMARKS 66 REFERENCES 66 4 IMPORTANCE
SAMPLING MONTE CARLO METHODS 6 8 4.1 INTRODUCTION 68 4.2 THE SIMPLEST
CASE: SINGLE SPIN-FLIP SAMPLING FOR THE SIMPLE ISING MODEL 69 4.2.1
ALGORITHM 70 4.2.2 BOUNDARY CONDITIONS 74 4.2.3 FINITE SIZE EFFECTS 77
4.2.4 FINITE SAMPLING TIME EFFECTS 90 4.2.5 CRITICAL RELAXATION 98 4.3
OTHER DISCRETE VARIABLE MODELS 105 4.3.1 ISING MODELS WITH COMPETING
INTERACTIONS 105 4.3.2 #-STATE POTTS MODELS 109 4.3.3 BAXTER AND
BAXTER-WU MODELS 110 4.3.4 CLOCK MODELS 111 4.3.5 ISING SPIN GLASS
MODELS 113 4.3.6 COMPLEX FLUID MODELS 114 4.4 SPIN-EXCHANGE SAMPLING 115
4.4.1 CONSTANT MAGNETIZATION SIMULATIONS 115 4.4.2 PHASE SEPARATION 115
4.4.3 DIFFUSION 117 4.4.4 HYDRODYNAMIC SLOWING DOWN 120 CONTENTS VLL 4.5
MICROCANONICAL METHODS 120 4.5.1 DEMON ALGORITHM 120 4.5.2 DYNAMIC
ENSEMBLE 121 4.5.3 Q2R 121 4.6 GENERAL REMARKS, CHOICE OF ENSEMBLE 122
4.7 STATICS AND DYNAMICS OF POLYMER MODELS ON LATTICES 122 4.7.1
BACKGROUND 122 4.7.2 FIXED BOND LENGTH METHODS 123 4.7.3 BOND
FLUCTUATION METHOD 125 4.7.4 ENHANCED SAMPLING USING A FOURTH DIMENSION
126 4.7.5 THE WORMHOLE ALGORITHM - ANOTHER METHOD TO EQUILIBRATE DENSE
POLYMERIC SYSTEMS 127 4.7.6 POLYMERS IN SOLUTIONS OF VARIABLE QUALITY:
OE-POINT, COLLAPSE TRANSITION, UNMIXING 128 4.7.7 EQUILIBRIUM POLYMERS: A
CASE STUDY 130 4.8 SOME ADVICE 133 REFERENCES 134 5 MORE ON IMPORTANCE
SAMPLING MONTE CARLO METHODS FOR LATTICE SYSTEMS 138 5.1 CLUSTER
FLIPPING METHODS 138 5.1.1 FORTUIN-KASTELEYN THEOREM 138 5.1.2
SWENDSEN-WANG METHOD 139 5.1.3 WOLFF METHOD 142 5.1.4 IMPROVED
ESTIMATORS 143 5.1.5 INVADED CLUSTER ALGORITHM 143 5.1.6 PROBABILITY
CHANGING CLUSTER ALGORITHM 144 5.2 SPECIALIZED COMPUTATIONAL TECHNIQUES
145 5.2.1 EXPANDED ENSEMBLE METHODS 145 5.2.2 MULTISPIN CODING 145 5.2.3
/V-FOLD WAY AND EXTENSIONS 146 5.2.4 HYBRID ALGORITHMS 149 5.2.5
MULTIGRID ALGORITHMS 149 5.2.6 MONTE CARLO ON VECTOR COMPUTERS 149 5.2.7
MONTE CARLO ON PARALLEL COMPUTERS 150 5.3 CLASSICAL SPIN MODELS 151
5.3.1 INTRODUCTION 151 5.3.2 SIMPLE SPIN-FLIP METHOD 152 5.3.3 HEATBATH
METHOD 154 5.3.4 LOW TEMPERATURE TECHNIQUES 155 5.3.5 OVER-RELAXATION
METHODS 155 5.3.6 WOLFF EMBEDDING TRICK AND CLUSTER FLIPPING 156 5.3.7
HYBRID METHODS 157 5.3.8 MONTE CARLO DYNAMICS VS. EQUATION OF MOTION
DYNAMICS 157 5.3.9 TOPOLOGICAL EXCITATIONS AND SOLITONS 158 VIII
CONTENTS 5.4 SYSTEMS WITH QUENCHED RANDOMNESS 160 5.4.1 GENERAL
COMMENTS: AVERAGING IN RANDOM SYSTEMS 160 5.4.2 PARALLEL TEMPERING: A
GENERAL METHOD TO BETTER EQUILIBRATE SYSTEMS WITH COMPLEX ENERGY
LANDSCAPES 165 5.4.3 RANDOM FIELDS AND RANDOM BONDS 165 5.4.4 SPIN
GLASSES AND OPTIMIZATION BY SIMULATED ANNEALING 166 5.4.5 AGEING IN SPIN
GLASSES AND RELATED SYSTEMS 171 5.4.6 VECTOR SPIN GLASSES: DEVELOPMENTS
AND SURPRISES 172 5.5 MODELS WITH MIXED DEGREES OF FREEDOM: SI/GE
ALLOYS, A CASE STUDY 173 5.6 SAMPLING THE FREE ENERGY AND ENTROPY 174
5.6.1 THERMODYNAMIC INTEGRATION 174 5.6.2 GROUNDSTATE FREE ENERGY
DETERMINATION 176 5.6.3 ESTIMATION OF INTENSIVE VARIABLES: THE CHEMICAL
POTENTIAL 177 5.6.4 LEE-KOSTERLITZ METHOD 177 5.6.5 FREE ENERGY FROM
FINITE SIZE DEPENDENCE AT T C 178 5.7 MISCELLANEOUS TOPICS 178 5.7.1
INHOMOGENEOUS SYSTEMS: SURFACES, INTERFACES, ETC. 178 5.7.2 OTHER MONTE
CARLO SCHEMES 184 5.7.3 INVERSE AND REVERSE MONTE CARLO METHODS 186
5.7.4 FINITE SIZE EFFECTS: A REVIEW AND SUMMARY 187 5.7.5 MORE ABOUT
ERROR ESTIMATION 188 5.7.6 RANDOM NUMBER GENERATORS REVISITED 190 5.8
SUMMARY AND PERSPECTIVE 19.1 REFERENCES 193 6 OFF-LATTICE MODELS 197 6.1
FLUIDS 197 6.1.1 NVT ENSEMBLE AND THE VIRIAL THEOREM 197 6.1.2 NP T
ENSEMBLE 200 6.1.3 GRAND CANONICAL ENSEMBLE 204 6.1.4 NEAR CRITICAL
COEXISTENCE: A CASE STUDY 208 6.1.5 SUBSYSTEMS: A CASE STUDY 210 6.1.6
GIBBS ENSEMBLE 215 6.1.7 WIDOM PARTICLE INSERTION METHOD AND VARIANTS
218 6.1.8 MONTE CARLO PHASE SWITCH 220 6.1.9 CLUSTER ALGORITHM FOR
FLUIDS 224 6.2 SHORT RANGE INTERACTIONS 225 6.2.1 CUTOFFS 225 6.2.2
VERLET TABLES AND CELL STRUCTURE 225 6.2.3 MINIMUM IMAGE CONVENTION 226
6.2.4 MIXED DEGREES OF FREEDOM RECONSIDERED 226 6.3 TREATMENT OF LONG
RANGE FORCES 226 6.3.1 REACTION FIELD METHOD 226 6.3.2 EWALD METHOD 227
6.3.3 FAST MULTIPOLE METHOD 228 CONTENTS IX 6.4 ADSORBED MONOLAYERS 229
6.4.1 SMOOTH SUBSTRATES 229 6.4.2 PERIODIC SUBSTRATE POTENTIALS 229 6.5
COMPLEX FLUIDS 231 6.5.1 APPLICATION OF THE LIU-LUIJTEN ALGORITHM TO A
BINARY FLUID MIXTURE 233 6.6 POLYMERS: AN INTRODUCTION 234 6.6.1 LENGTH
SCALES AND MODELS 234 6.6.2 ASYMMETRIC POLYMER MIXTURES: A CASE STUDY
241 6.6.3 APPLICATIONS: DYNAMICS OF POLYMER MELTS; THIN ADSORBED
POLYMERIC FILMS 245 6.6.4 POLYMER MELTS: SPEEDING UP BOND FLUCTUATION
MODEL SIMULATIONS 248 6.7 CONFIGURATIONAL BIAS AND SMART MONTE CARLO
250 6.8 OUTLOOK 253 REFERENCES 253 7 REWEIGHTING METHODS 257 7.1
BACKGROUND 257 7.1.1 DISTRIBUTION FUNCTIONS 257 7.1.2 UMBRELLA SAMPLING
257 7.2 SINGLE HISTOGRAM METHOD: THE ISING MODEL AS A CASE STUDY 260 7.3
MULTIHISTOGRAM METHOD 267 7.4 BROAD HISTOGRAM METHOD 268 7.5 TRANSITION
MATRIX MONTE CARLO 268 7.6 MULTICANONICAL SAMPLING 269 7.6.1 THE
MULTICANONICAL APPROACH AND ITS RELATIONSHIP TO CANONICAL SAMPLING 269
7.6.2 NEAR FIRST ORDER TRANSITIONS 270 7.6.3 GROUNDSTATES IN COMPLICATED
ENERGY LANDSCAPES 272 7.6.4 INTERFACE FREE ENERGY ESTIMATION 273 7.7 A
CASE STUDY: THE CASIMIR EFFECT IN CRITICAL SYSTEMS 274 7.8 WANG-LANDAU
SAMPLING 276 7.8.1 BASIC ALGORITHM 276 7.8.2 APPLICATIONS TO MODELS
WITH CONTINUOUS VARIABLES 279 7.8.3 CASE STUDIES WITH TWO-DIMENSIONAL
WANG-LANDAU SAMPLING 279 7.8.4 BACK TO NUMERICAL INTEGRATION 279 7.9 A
CASE STUDY: EVAPORATION/CONDENSATION TRANSITION OF DROPLETS 281
REFERENCES 282 8 QUANTUM MONTE CARLO METHODS 285 8.1 INTRODUCTION 285
8.2 FEYNMAN PATH INTEGRAL FORMULATION 287 8.2.1 OFF-LATTICE PROBLEMS:
LOW-TEMPERATURE PROPERTIES OF CRYSTALS 287 8.2.2 BOSE STATISTICS AND
SUPERFLUIDITY 293 X CONTENTS 8.2.3 PATH INTEGRAL FORMULATION FOR
ROTATIONAL DEGREES OF FREEDOM 294 8.3 LATTICE PROBLEMS 297 8.3.1 THE
ISING MODEL IN A TRANSVERSE FIELD 297 8.3.2 ANISOTROPIC HEISENBERG CHAIN
298 8.3.3 FERMIONS ON A LATTICE 302 8.3.4 AN INTERMEZZO: THE MINUS SIGN
PROBLEM 304 8.3.5 SPINLESS FERMIONS REVISITED 306 8.3.6 CLUSTER METHODS
FOR QUANTUM LATTICE MODELS 310 8.3.7 CONTINUOUS TIME SIMULATIONS 310
8.3.8 DECOUPLED CELL METHOD 311 8.3.9 HANDSCOMB S METHOD 312 8.3.10
WANG-LANDAU SAMPLING FOR QUANTUM MODELS 313 8.3.11 FERMION DETERMINANTS
314 8.4 MONTE CARLO METHODS FOR THE STUDY OF GROUNDSTATE PROPERTIES 316
8.4.1 VARIATIONAL MONTE CARLO (VMC) 316 8.4.2 GREEN S FUNCTION MONTE
CARLO METHODS (GFMC) 318 8.5 CONCLUDING REMARKS 320 REFERENCES 321 9
MONTE CARLO RENORMALIZATION GROUP METHODS 324 9.1 INTRODUCTION TO
RENORMALIZATION GROUP THEORY 324 9.2 REAL SPACE RENORMALIZATION GROUP
328 9.3 MONTE CARLO RENORMALIZATION GROUP 329 9.3.1 LARGE CELL
RENORMALIZATION 329 9.3.2 MA S METHOD: FINDING CRITICAL EXPONENTS AND
THE FIXED POINT HAMILTONIAN 331 9.3.3 SWENDSEN S METHOD 332 9.3.4
LOCATION OF PHASE BOUNDARIES 334 9.3.5 DYNAMIC PROBLEMS: MATCHING
TIME-DEPENDENT CORRELATION FUNCTIONS 335 9.3.6 INVERSE MONTE CARLO
RENORMALIZATION GROUP TRANSFORMATIONS 336 REFERENCES 336 10
NON-EQUILIBRIUM AND IRREVERSIBLE PROCESSES 338 10.1 INTRODUCTION AND
PERSPECTIVE 338 10.2 DRIVEN DIFFUSIVE SYSTEMS (DRIVEN LATTICE GASES) 338
10.3 CRYSTAL GROWTH 341 10.4 DOMAIN GROWTH 344 10.5 POLYMER GROWTH 347
10.5.1 LINEAR POLYMERS 347 10.5.2 GELATION 347 10.6 GROWTH OF STRUCTURES
AND PATTERNS 349 10.6.1 EDEN MODEL OF CLUSTER GROWTH 349 10.6.2
DIFFUSION LIMITED AGGREGATION 349 CONTENTS XI 10.6.3 CLUSTER-CLUSTER
AGGREGATION 352 10.6.4 CELLULAR AUTOMATA 352 10.7 MODELS FOR FILM GROWTH
353 10.7.1 BACKGROUND 353 10.7.2 BALLISTIC DEPOSITION 354 10.7.3
SEDIMENTATION 355 10.7.4 KINETIC MONTE CARLO AND MBE GROWTH 356 10.8
TRANSITION PATH SAMPLING 358 10.9 FORCED POLYMER PORE TRANSLOCATION: A
CASE STUDY 359 10.10 OUTLOOK: VARIATIONS ON A THEME 362 REFERENCES 362
11 LATTICE GAUGE MODELS: A BRIEF INTRODUCTION 365 11.1 INTRODUCTION:
GAUGE INVARIANCE AND LATTICE GAUGE THEORY 365 11.2 SOME TECHNICAL
MATTERS 367 11.3 RESULTS FOR Z(N) LATTICE GAUGE MODELS 367 11.4 COMPACT
U(L) GAUGE THEORY 368 11.5 SU(2) LATTICE GAUGE THEORY 369 11.6
INTRODUCTION: QUANTUM CHROMODYNAMICS (QCD) AND PHASE TRANSITIONS OF
NUCLEAR MATTER 370 11.7 THE DECONFINEMENT TRANSITION OF QCD 372 11.8
TOWARDS QUANTITATIVE PREDICTIONS 375 REFERENCES 377 12 A BRIEF REVIEW OF
OTHER METHODS OF COMPUTER SIMULATION 379 12.1 INTRODUCTION 379 12.2
MOLECULAR DYNAMICS 379 12.2.1 INTEGRATION METHODS (MICROCANONICAL
ENSEMBLE) 379 12.2.2 OTHER ENSEMBLES (CONSTANT TEMPERATURE, CONSTANT
PRESSURE, ETC.) 383 12.2.3 NON-EQUILIBRIUM MOLECULAR DYNAMICS 386 12.2.4
HYBRID METHODS (MD + MC) 386 12.2.5 AB INITIO MOLECULAR DYNAMICS 387
12.2.6 HYPERDYNAMICS AND METADYNAMICS 388 12.3 QUASI-CLASSICAL SPIN
DYNAMICS 388 12.4 LANGEVIN EQUATIONS AND VARIATIONS (CELL DYNAMICS) 392
12.5 MICROMAGNETICS 393 12.6 DISSIPATIVE PARTICLE DYNAMICS (DPD) 393
12.7 LATTICE GAS CELLULAR AUTOMATA 395 12.8 LATTICE BOLTZMANN EQUATION
395 12.9 MULTISCALE SIMULATION 396 REFERENCES 398 13 MONTE CARLO
SIMULATIONS AT THE PERIPHERY OF PHYSICS AND BEYOND 401 13.1 COMMENTARY
401 13.2 ASTROPHYSICS 401 XII CONTENTS 13.3 MATERIALS SCIENCE 13.4
CHEMISTRY 13.5 BIOLOGICALLY INSPIRED PHYSICS 13.5.1 COMMENTARY AND
PERSPECTIVE 13.5.2 LATTICE PROTEINS 13.5.3 CELL SORTING 13.6 BIOLOGY
13.7 MATHEMATICS/STATISTICS 13.8 SOCIOPHYSICS 13.9 ECONOPHYSICS 13.10
TRAFFIC SIMULATIONS 13.11 MEDICINE 13.12 NETWORKS: WHAT CONNECTIONS
REALLY MATTER? 13.13 FINANCE REFERENCES 14 MONTE CARLO STUDIES OF
BIOLOGICAL MOLECULES 14.1 INTRODUCTION 14.2 PROTEIN FOLDING 14.2.1
INTRODUCTION 14.2.2 HOW TO BEST SIMULATE PROTEINS: MONTE CARLO MOLECULAR
DYNAMICS 14.2.3 GENERALIZED ENSEMBLE METHODS 14.2.4 GLOBULAR PROTEINS: A
CASE STUDY 14.2.5 SIMULATIONS OF MEMBRANE PROTEINS 14.3 MONTE CARLO
SIMULATIONS OF CARBOHYDRATES 14.4 DETERMINING MACROMOLECULAR STRUCTURES
14.5 OUTLOOK REFERENCES 15 OUTLOOK APPENDIX: LISTING OF PROGRAMS
MENTIONED IN THE TEXT INDEX
|
| any_adam_object | 1 |
| author | Landau, David P. 1941- Binder, Kurt 1944-2022 |
| author_GND | (DE-588)131584960 (DE-588)115452907 |
| author_facet | Landau, David P. 1941- Binder, Kurt 1944-2022 |
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| ctrlnum | (OCoLC)500243508 (DE-599)BVBBV035864100 |
| dewey-full | 530.1322 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 530 - Physics |
| dewey-raw | 530.1322 |
| dewey-search | 530.1322 |
| dewey-sort | 3530.1322 |
| dewey-tens | 530 - Physics |
| discipline | Physik Mathematik |
| edition | 3. ed. |
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| id | DE-604.BV035864100 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:06:27Z |
| institution | BVB |
| isbn | 9780521768481 9781107649804 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018721879 |
| oclc_num | 500243508 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-20 DE-29T DE-11 DE-739 DE-703 DE-91G DE-BY-TUM DE-83 |
| owner_facet | DE-19 DE-BY-UBM DE-20 DE-29T DE-11 DE-739 DE-703 DE-91G DE-BY-TUM DE-83 |
| physical | XV, 471 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spelling | Landau, David P. 1941- Verfasser (DE-588)131584960 aut A guide to Monte Carlo simulations in statistical physics David P. Landau ; Kurt Binder 3. ed. Cambridge [u.a.] Cambridge Univ. Press 2009 XV, 471 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Monte Carlo method Statistical physics Statistische Physik (DE-588)4057000-9 gnd rswk-swf Monte-Carlo-Simulation (DE-588)4240945-7 gnd rswk-swf Monte-Carlo-Simulation (DE-588)4240945-7 s Statistische Physik (DE-588)4057000-9 s DE-604 Binder, Kurt 1944-2022 Verfasser (DE-588)115452907 aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018721879&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Landau, David P. 1941- Binder, Kurt 1944-2022 A guide to Monte Carlo simulations in statistical physics Monte Carlo method Statistical physics Statistische Physik (DE-588)4057000-9 gnd Monte-Carlo-Simulation (DE-588)4240945-7 gnd |
| subject_GND | (DE-588)4057000-9 (DE-588)4240945-7 |
| title | A guide to Monte Carlo simulations in statistical physics |
| title_auth | A guide to Monte Carlo simulations in statistical physics |
| title_exact_search | A guide to Monte Carlo simulations in statistical physics |
| title_full | A guide to Monte Carlo simulations in statistical physics David P. Landau ; Kurt Binder |
| title_fullStr | A guide to Monte Carlo simulations in statistical physics David P. Landau ; Kurt Binder |
| title_full_unstemmed | A guide to Monte Carlo simulations in statistical physics David P. Landau ; Kurt Binder |
| title_short | A guide to Monte Carlo simulations in statistical physics |
| title_sort | a guide to monte carlo simulations in statistical physics |
| topic | Monte Carlo method Statistical physics Statistische Physik (DE-588)4057000-9 gnd Monte-Carlo-Simulation (DE-588)4240945-7 gnd |
| topic_facet | Monte Carlo method Statistical physics Statistische Physik Monte-Carlo-Simulation |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018721879&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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