![]() Number generator, based on the Hammersley points, to uniformly sample a unit hypercube. Hammersley sampling belongs to the category of quasi-Monte Carlo methods. This will resolve all the effects and interactions. Information matrix that is inverted during the Fit’s regression analysis, which in turn improves the numerical efficiency of the DOE.Ī factorial experiment in which only a chosen fraction of the combinations required for the Full Factorial DOE is run.Įvaluates all possible combinations of input variable levels. By identifying the type of regression that will be used, samples are selected to maximize the determinant of the Primarily intended to be used as the input matrix for a Least Squares Regression Fit. Generates higher order response surfaces using fewer required runs than a normal factorial.Ĭentral Composite Design contains an imbedded factorial or fractional factorial design with center points that areĪugmented with a group of `star points' that allow the estimation of curvature. Learn how to create, open, import and save models.īefore you can create approaches you must first setup your Study by defining input variables and output responses.ĭefine the models, input variables, and output responses to be used in the study. ![]() Learn the basics and discover the workspace.ĭiscover HyperStudy functionality with interactive tutorials. A Latin HyperCube DOE, categorized as a space filling DOE, is the generalization of this concept to an arbitrary number of dimensions. Numerical methods available for a DOE approach.Ī square grid containing sample positions is a Latin square if, and only if, there is only one sample in each rowĪnd each column. Select a numerical method to use when evaluating the DOE. By running a DOE, you can determine which factors are most influential on an output response. To investigate their effect upon the output responses and to get an understanding of the global behavior Is a specific set of steps taken to study the mathematical model of a design.Ī DOE is a series of tests in which purposeful changes are made to the input variables Once the study Setup is complete, an unlimited combination of approaches can be added to a study. Decomposition of variance įrom a black box perspective, any model may be viewed as a function Y= f( X), where X is a vector of d uncertain model inputs įor the estimation of the S i and the S Ti respectively.įor the estimation of the S i and the S Ti for all input variables, N( d+2) model runs are required.A study is a self-contained project in which models, variables, output responses, and approaches are defined. it is a global method), they can deal with nonlinear responses, and they can measure the effect of interactions in non- additive systems. Variance-based measures of sensitivity are attractive because they measure sensitivity across the whole input space (i.e. These percentages are directly interpreted as measures of sensitivity. For example, given a model with two inputs and one output, one might find that 70% of the output variance is caused by the variance in the first input, 20% by the variance in the second, and 10% due to interactions between the two. Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs. ![]() Sobol’) is a form of global sensitivity analysis. Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. ![]()
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