เปรียบเทียบวิธี

	การออกแบบการทดลองโดยใช้การจำลองช่วย (Simulation-Assisted Design of Experiments)	การสุ่มตัวอย่างแบบละตินไฮเปอร์คิวบ์
สาขาวิชา	การออกแบบการทดลอง	การจำลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1970s–1990s (formalized with computer experimentation growth)	1979
ผู้ริเริ่ม	Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.	—
ประเภท	Hybrid experimental-computational method	Stratified space-filling sampling design
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE	LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design
ที่เกี่ยวข้อง	5	4
สรุป	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.