Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño de Experimentos Asistido por Simulación× | Muestreo de Hipercubo Latino× | |
|---|---|---|
| Campo≠ | Diseño experimental | Simulación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1970s–1990s (formalized with computer experimentation growth) | 1979 |
| Autor original≠ | Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al. | — |
| Tipo≠ | Hybrid experimental-computational method | Stratified space-filling sampling design |
| Fuente seminal≠ | 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 ↗ |
| Alias | Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE | LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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