Optimal sliced latin hypercube designs
WebLatin Hypercube design was also prepared with The computed maxima for each model are given optimal spacing. The reason for choosing the Latin in Table 7 along with the temperatures at which Hypercube was that it can be implemented in a they occurred. WebThis article proposes a method for constructing a new type of space-filling design, called a sliced Latin hypercube design, intended for running computer experiments. Such a design …
Optimal sliced latin hypercube designs
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WebIn the Optimal Latin Hypercube technique the design space for each factor is divided uniformly (the same number of divisions, n n, for all factors). These levels are randomly combined to generate a random Latin Hypercube as the initial DOE design matrix with n n points (each level of a factor studies only once). WebOptimal Sliced Latin Hypercube Designs Shan BA, William R. MYERS,andWilliamA.BRENNEMAN The Procter and Gamble Company, Mason, OH 45040 …
WebDownloadable! Sliced Latin hypercube designs (SLHDs) are widely used in computer experiments with both quantitative and qualitative factors and in batches. Optimal SLHDs achieve better space-filling property on the whole experimental region. However, most existing methods for constructing optimal SLHDs have restriction on the run sizes. In this … WebOptimal Sliced Latin Hypercube Designs Shan Ba, William R. Myers, and William A. Brenneman The Procter and Gamble Company, Mason, OH 45040 ([email protected]; …
WebMay 2, 2024 · This function utilizes a version of the simulated annealing algorithm and several computational shortcuts to efficiently generate the optimal Latin Hypercube Designs (LHDs) and the optimal Sliced Latin Hypercube Designs (SLHDs). The maximin distance criterion is adopted as the optimality criterion. WebOct 24, 2024 · Recently, the construction of nested or sliced Latin hypercube designs (LHDs) has received notable interest for planning computer experiments with special combinational structures. In this paper, we propose an approach to constructing nested and/or sliced LHDs by using small LHDs and structural vectors/matrices. This method is …
WebApr 1, 1994 · In this paper, optimal Latin-hypercube designs minimizing IMSE or maximizing entropy are considered. These designs turn out to be well spread over the design region without replicated coordinate values, often symmetric, and nearly optimal among all Latin-hypercube designs. A 2-stage (exchange- and Newton-type) computational algorithm for ...
Web6 rows · Sep 16, 2024 · Latin hypercube designs (LHDs) [ 1] are widely used in computer experiments because of their ... tenya iida mangaWebOct 19, 2024 · Abstract: As accuracy of optimization can not be guaranteed without high-quality samples, the distribution of a finite number of evaluation points where experiments should be conducted in design space is an important issue, particularly when the experiment to obtain sample is expensive. To utilize limited number of evaluation points to represent … tenya iida laptop wallpaperWebSliced Latin hypercube designs (SLHDs) have important applications in designing computer experiments with continuous and categorical factors. However, a randomly generated … tenya iida my hero academia wikiWebThis function utilizes a version of the simulated annealing algorithm and several computational shortcuts to efficiently generate the optimal Latin Hypercube Designs … tenya iida name meaningWebSLHD: Maximin-Distance (Sliced) Latin Hypercube Designs Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the … tenya iida & mei hatsumeWebLatin hypercube samples are non-collapsing. Figure 3(a) illustrates the case of a Latin hypercube design with d=3 dimensions and p=15 points. Any of the two-dimensional projections is still a Latin hypercube design with the same p=15 points (although, for this particular case, the x 1 x 2 projection is the best in terms of space filling). Thus ... tenya iida morreWebJul 18, 2024 · Sliced Latin hypercube designs (SLHDs), proposed by Qian ( 2012 ), are widely used in computer experiments with qualitative and quantitative factors, model calibration, cross validation, multiple experiments, stochastic optimization and data pooling. tenya iida or lida