Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Проектирование экспериментов с поддержкой симуляции× | Методология поверхности отклика (RSM)× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство≠ | Process / pipeline | Hypothesis test |
| Год появления≠ | 1970s–1990s (formalized with computer experimentation growth) | 1951 |
| Автор метода≠ | Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al. | George E. P. Box & K. B. Wilson |
| Тип≠ | Hybrid experimental-computational method | Second-order polynomial response surface model |
| Основополагающий источник≠ | Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202 | Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45. link ↗ |
| Другие названия≠ | Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Связанные≠ | 5 | 7 |
| Сводка≠ | Simulation-assisted design of experiments (SA-DoE) integrates computational simulation tools — such as finite element analysis (FEA), computational fluid dynamics (CFD), or discrete-event simulation — with classical DoE principles to systematically explore the factor space of a system. Rather than running costly or hazardous physical trials, researchers execute a structured set of virtual experiments across selected factor combinations, then fit a surrogate model to the simulation outputs to understand main effects, interactions, and optimal settings. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
| ScholarGateНабор данных ↗ |
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