Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño Box-Behnken Asistido por Simulación× | Muestreo de Hipercubo Latino× | |
|---|---|---|
| Campo≠ | Diseño experimental | Simulación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1960 (base design); simulation-assisted application developed from the 1990s onward | 1979 |
| Autor original≠ | Box-Behnken (1960) for the base design; simulation integration emerged from computer experiment methodology in the 1980s-2000s | — |
| Tipo≠ | Simulation-integrated response surface design | Stratified space-filling sampling design |
| Fuente seminal≠ | Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455-475. DOI ↗ | 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 ↗ |
| Alias | SA-BBD, computer-aided Box-Behnken design, simulation-based BBD, virtual Box-Behnken design | LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design |
| Relacionados | 4 | 4 |
| Resumen≠ | Simulation-assisted Box-Behnken design couples the three-level, near-spherical Box-Behnken experimental matrix with computer simulation models — such as finite-element analysis, computational fluid dynamics, or discrete-event simulation — to map how multiple controllable factors jointly affect one or more output responses, while eliminating the need for costly or hazardous physical prototype fabrication at every design point. | 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. |
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