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仿真方法

91 种方法属于此方法族。

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Agent-Based Cellular AutomataAgent-Based Cellular Automata (ABCA) is a hybrid simulation framework that integrates the local transition rules of cellular automata with the autonomous behavioral logic of agent-Agent-Based Discrete-Event SimulationAgent-based discrete-event simulation (AB-DES) is a hybrid modeling paradigm that couples autonomous agent behavior with an event-driven execution engine. It captures the decision-Agent-Based Markov ModelThe Agent-Based Markov Model (ABMM) is a hybrid simulation framework that embeds Markov chain state-transition logic inside individual autonomous agents. Each agent independently sAgent-Based MicrosimulationAgent-based microsimulation (ABMS) merges traditional microsimulation's individual-level statistical tracking with agent-based modeling's behavioral rules and interaction mechanism基于主体的建模（ABM）Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior 基于代理的多目标优化Agent-based multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or

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This topic's most-referenced foundational methods, in the order they were developed — a place to start if you're new here.

  1. 多目标优化1896 (concept); 1989–2002 (evolutionary algorithms era)by Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
  2. 马尔可夫模型1906by Andrei Markov
  3. 马尔可夫链蒙特卡洛 (MCMC)1953 (Metropolis-Hastings); 1984 (Gibbs)by Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)
  4. 离散事件仿真 (DES)1960s (formalized); modern computational form from 1970s onwardby Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)
  5. 系统动力学1961by Jay W. Forrester
  6. 政策情景分析1967–1990sby Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD
  7. 基于主体的建模（ABM）1970s–1990s (formalized as a field)by Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
  8. 拉丁超立方体采样1979
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全部方法 91

Agent-Based Cellular AutomataAgent-Based Discrete-Event SimulationAgent-Based Markov ModelAgent-Based Microsimulation基于主体的建模（ABM）基于代理的多目标优化Agent-Based Scenario AnalysisAgent-Based Sensitivity Analysis基于主体的系统动力学元胞自动机确定性基于主体的建模确定性元胞自动机确定性离散事件仿真确定性马尔可夫模型确定性微观模拟确定性多目标优化确定性情景分析确定性敏感性分析确定性系统动力学数字孪生仿真离散选择模拟离散事件仿真 (DES)离散事件系统仿真集合卡尔曼滤波器分形分析Geant4 模拟全局敏感性分析Importance Sampling伊辛模型蒙特卡洛拉丁超立方体采样Longstaff-Schwartz 方法马尔可夫链蒙特卡洛 (MCMC)马尔可夫模型个体模拟蒙特卡洛中子与粒子输运蒙特卡洛工艺变化分析多目标基于主体的建模多目标元胞自动机多目标离散事件仿真多目标马尔可夫模型多目标微观模拟多目标优化多目标情景分析多目标敏感性分析多目标系统动力学路径积分蒙特卡洛政策情景多主体建模政策情景分析政策情景元胞自动机政策情景离散事件仿真政策情景微观模拟政策情景蒙特卡洛模拟策略情景多目标优化政策情景敏感性分析政策情景系统动力学量子蒙特卡洛循环量化分析 (RQA)鲁棒性基于智能体的建模稳健离散事件仿真稳健马尔可夫模型稳健微观模拟鲁棒多目标优化稳健情景分析稳健性敏感性分析样本熵情景分析与假设模拟自组织临界性模拟辅助验证性研究仿真辅助控制图模拟辅助事件树分析仿真辅助失效模式与影响分析模拟辅助故障树分析模拟辅助假设检验研究Simulation-Assisted Process Capability Analysis模拟辅助定量内容分析基于仿真的可靠性分析Simulation-Assisted Statistical Process Control模拟辅助趋势研究随机元胞自动机随机微分方程 (SDEs)随机离散事件仿真随机马尔可夫模型随机微观模拟随机多目标优化随机情景分析Stochastic Sensitivity Analysis随机系统动力学系统动力学VaR（风险价值）蒙特卡洛模拟的方差缩减技术Vegas Monte Carlo