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| 內容簡介: |
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统计物理学中的蒙特卡洛模拟主要处理凝聚态物理学的多体系统和相关物理学、化学及其他方面的计算模拟,甚至渗透到交通流、股票市场波动等等领域。书中描述了多变量蒙特卡洛模拟方法的理论背景,给出了初学者学习进行模拟和结果分析的系统演示。《统计物理学中的蒙特卡罗模拟(第5版,英文版)》是第五版,不仅包括经典方法,也包括蒙特卡洛模拟方法;增加了一章专门讲述自由能景观采样。
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| 目錄:
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1Introduction:PurposeAndScopeofThisVolume,AndSomeGeneralComments
2TheoreticalFoundationsofTheMonteCarloMethodAndItsApplicationsInStatisticalPhysics
2.1SimpleSamplingVersusImportanceSampling
2.L.1Models
2.1.2SimpleSampling
2.1.3RandomWalksandSelf-AvoidingWalks
2.1.4ThermalAveragesBytheSimpleSamplingMethod
2.1.5AdvantagesandLimitationsofSimpleSampling
2.1.6ImportanceSampling
2.1.7MoreAboutModelsAndAlgorithms
2.2OrganizationofMonteCarloPrograms,andtheDynamicInterpretationofMonteCarloSampling
2.2.1FirstCommentsonTheSimulationofTheIsingModel
2.2.2BoundaryConditions
2.2.3TheDynamicInterpretationofTheImportanceSamplingMonteCarloMethod
2.2.4StatisticalErrorsandTime-DisplacedRelaxationFunctions
2.3Finite-SizeEffects
2.3.1Finite-SizeEffectsAtThePercolationTransition
2.3.2Finite-SizeScalingForThePercolationProblem
2.3.3BrokenSymmetryAndFinite-SizeEffectsAtThermalPhaseTransitions
2.3.4TheOrderParameterProbabilityDistributionAndItsUsetoJustifyFinite-SizeScalingAndPhenomenologicalRenormalization
2.3.5Finite-SizeBehaviorofRelaxationTimes
2.3.6Finite-SizeScalingWithout"Hyperscaling".
2.3.7Finite-SizeScalingForFirst-OrderPhaseTransitions
2.3.8Finite-SizeBehaviorofStatisticalErrorsAndtheProblemOfSelf-Averaging
2.4RemarksonTheScopeofTheTheoryChapter
3GuidetoPracticalWorkWithTheMonteCarloMethod
3.1AimsofTheGuide
3.2SimpleSampling
3.2.1RandomWalk
3.2.2NonreversalRandomWalk
3.2.3Self-AvoidingRandomWalk
3.2.4Percolation
3.3BiasedSampling
3.3.1Self-AvoidingRandomWalk
3.4ImportanceSampling
3.4.1IsingModel
3.4.2Self-AvoidingRandomWalk
4SomeImportantRecentDevelopmentsOfTheMonteCarloMethodology
4.1Introduction
4.2ApplicationoftheSwendsen-WangClusterAlgorithmToTheIsingModel
4.3ReweightingMethodsInTheStudyOfPhaseDiagrams,First-OrderPhaseTransitions,AndInterfacialTensions
4.4SomeCommentsOnAdvancesWithFinite-SizeScalingAnalyses
5QuantumMonteCarloSimulations:AnIntroduction
5.1QuantumStatisticalMechanicsVersusClassicalStatisticalMechanics
5.2ThePathIntegralQuantumMonteCarloMethod
5.3QuantumMonteCarloForLatticeModels
5.4ConcludingRemarks
6MonteCarloMethodsForTheSamplingofFreeEnergyLandscapes.
6.1IntroductionAndOverview
6.2UmbrellaSampling
6.3MulticanonicalSamplingAndOther"ExtendedEnsemble"Methods
6.4Wang-LandauSampling
6.5TransitionPathSampling
6.6ConcludingRemarks
Appendix
A.1AlgorithmForTheRandomWalkProblem
A.2AlgorithmForClusterIdentification
References
Bibliography
SubjectIndex
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