. As a novel metamodel we introduce intrinsic Kriging, for either deterministic or random simulation. For deterministic … simulation we study the famous `efficient global optimization' (EGO) method, substituting intrinsic Kriging for universal Kriging … Kriging; (2) this variant uses a different procedure to allocate the total available number of replications over simulated …

. We formulate this intrinsic Kriging as a metamodel in deterministic and random simulation models. For random simulation … simulations. These experiments suggest that intrinsic Kriging gives more accurate metamodel, in most experiments. …We derive intrinsic Kriging, using Matherons intrinsic random functions which eliminate the trend in classic Kriging …

Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis …, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear … independently distributed with a constant variance; moreover, the regression (meta)model of the simulation model’s I/O behaviour is …

bifurcation" method. Furthermore, it reviews Kriging metamodels and their designs. It mentions that sensitivity analysis may also …

This article uses a sequentialized experimental design to select simulation input com- binations for global … optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the … input/output data of the simulation model (computer code). This design and analysis adapt the clas- sic "expected …

Design Of Experiments (DOE) is needed for experiments with real-life systems, and with either deterministic or random … Bifurcation. Then it discusses Kriging including Latin Hyper cube Sampling and sequential designs. It ends with optimization … through Generalized Response Surface Methodology and Kriging combined with Mathematical Programming, including Taguchian …

optimal combination? To answer these questions, the paper uses parametric bootstrapped Kriging and "conditional simulation …". Classic Kriging estimates the variance of its predictor by plugging-in the estimated GP parameters so this variance is biased …. The main conclusion is that classic Kriging seems quite robust; i.e., classic Kriging gives acceptable confidence …

Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input …/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not …? Classic Kriging simply plugs the estimated Kriging parameters into the formula for the predictor variance, so theoretically …

Textbooks on Design of Experiments invariably start by explaining why one-factor-at-a-time (OAT) is an inferior method …