方法对比
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| 全局敏感性分析× | 实验设计× | |
|---|---|---|
| 领域≠ | 仿真 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1973–2001 | 1935 |
| 提出者≠ | I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973) | Ronald A. Fisher |
| 类型≠ | Variance-based sensitivity decomposition | Experimental planning framework |
| 开创性文献≠ | Sobol, I.M. (2001). Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates. Mathematics and Computers in Simulation, 55(1–3), 271–280. DOI ↗ | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| 别名≠ | variance decomposition, Sobol indices, Morris screening, FAST method | DOE, experimental design, factorial experimentation, planned experimentation |
| 相关≠ | 4 | 3 |
| 摘要≠ | Global sensitivity analysis (GSA) is a family of techniques that decompose the variance of a model's output across its input parameters, quantifying how much each input — and each combination of inputs — contributes to the total uncertainty in the result. Sobol's variance-based indices (2001), Morris's one-at-a-time (OAT) screening (1991), and the Fourier Amplitude Sensitivity Test (FAST, first proposed by Cukier et al. in 1973) are the three most widely used approaches. Together they serve as the standard toolkit for identifying which parameters drive model behaviour and which can be safely fixed. | Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences. |
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