Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Проектирование экспериментов с поддержкой симуляции× | Латинское гиперкубическое проектирование× | |
|---|---|---|
| Область≠ | Планирование эксперимента | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1970s–1990s (formalized with computer experimentation growth) | 1979 |
| Автор метода≠ | Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al. | — |
| Тип≠ | Hybrid experimental-computational method | Stratified space-filling sampling design |
| Основополагающий источник≠ | Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202 | McKay, M.D., Beckman, R.J. & Conover, W.J. (1979). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2), 239-245. DOI ↗ |
| Другие названия | Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE | LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design |
| Связанные≠ | 5 | 4 |
| 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. | Latin Hypercube Sampling (LHS) is a stratified space-filling design for computer experiments, introduced by McKay, Beckman, and Conover in 1979. It divides each input variable's range into equally probable strata and draws exactly one sample per stratum, ensuring that the full input space is covered with far fewer model evaluations than standard Monte Carlo simulation requires. It is routinely paired with global sensitivity analysis — particularly Sobol indices — to quantify how much each input drives output variability. |
| ScholarGateНабор данных ↗ |
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