方法对比
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| 敏感性分析集成全因子设计× | 实验设计× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s (formalized combination) | 1935 |
| 提出者≠ | Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s) | Ronald A. Fisher |
| 类型≠ | Experimental design with factor importance ranking | Experimental planning framework |
| 开创性文献≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons. ISBN: 978-0470059975 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| 别名 | SA-FFD, full factorial design with sensitivity analysis, factorial-based sensitivity analysis, FFD-SA | DOE, experimental design, factorial experimentation, planned experimentation |
| 相关 | 3 | 3 |
| 摘要≠ | Sensitivity analysis-integrated full factorial design combines exhaustive factorial experimentation — where every combination of factor levels is tested — with systematic sensitivity analysis to quantify how much each input factor drives variation in the output response. This hybrid approach provides both reliable effect estimates and a ranked picture of factor importance, guiding engineers and scientists toward the levers that truly matter for system performance. | 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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