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1=Approximation is not configured, no input parameters
2=Approximation is not configured, no output parameters
3=Could not evaluate Universal Kriging approximation
4=Could not evaluate RSM approximation
5=RSM initialization cancelled
6=Could not calculate minimum number of designs
7=Could not calculate recommended number of designs
8=Reading Kriging coefficients from coefficients data file
9=Kriging initialization cancelled
10=Kriging initialization failed
11=Initializing Universal Kriging...
12=Failed to initialize Universal Kriging approximation
13=Calculating coefficients
14=Failed to initialize approximation
15=Initialization cancelled
16=Initialization failed
17=Calculating Universal Kriging
18=Failed to initialize approximation
19=Input Parameter Scaling:
20=Correlation Function:
21=Alpha:
22=Minimum Distance between Points:
23=Mean Zero Standardization
24=Min-Max Normalization
25=Gaussian
26=Exponential
27=Cubic Spline
28=Matern Linear
29=Matern Cubic
30=Five correlations are available: Gaussian, Exponential, Cubic Spline, Matern Linear and Matern Cubic
31=Choose the auto-correlation function for the fit
32=Kriging Technique Options
33=Reading Universal Kriging coefficients from coefficients data file
34=Universal Kriging Technique Options
35=Could not get Universal Kriging configuration from plugin
36=Universal Kriging initialization cancelled
37=Universal Kriging initialization failed

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desc=Universal Kriging approximation technique plugin
dispname=Universal Kriging Model
techniqueoptionsdesc=<b>Correlation Function:</b> <ul><li>Gaussian - Typically used for approximating smooth functions; however, produces a poor fit when sampling points are too close. </li><li>Exponential - If the sample points are close, use the Exponential correlation function.</li><li>Cubic Spline - Use the Cubic Spline correlation function to correlate data that does follow a specific pattern.</li><li>Matern Linear - Use the Matern Linear correlation function if the Gaussian and Matern Cubic correlation functions produced an unacceptable fit.</li> <li>Matern Cubic - Typically, Matern Cubic correlation function is more accurate than the Matern Linear. </li> </ul><p><b>	Alpha : </b>  uses the value of Alpha to relax the requirement that the Universal Kriging model approximation pass through every single data point. All points that are closer than the value of Alpha are removed from the sample set before fitting. By not going through every point, Isight can effectively smooth noisy functions and provide an approximation that may be easier to optimize. Enter a value of zero to stop the conditioning of the matrix.  <br><p><b> Minimum distance between points: </b> Occasionally, when points are clustered together the matrices used in fitting the Kriging model become ill-conditioned resulting in a poor fit. You can filter points from the sample based on distance to avoid a poor fit. All points that are closer than the Minimum distance between points are removed from the sample set before fitting.<p><b>Input Parameter Scaling:</b> <ul><li>Min-Max Normalization - Normalize the input parameter values between zero and one. </li><li>Mean Zero Standardization - Rescale the input parameter values to a mean of zero with unit variance.</li>
longdesc=The Universal Kriging model is an interpolation method that converts partial observations of a spatial field to predictions of that field at unobserved locations. The model is useful in predicting temporally and spatially correlated data and typically creates a good approximation in cases with a small number of data points. <p>The Kriging model is very flexible and allows you to choose between a wide range of correlation functions for building the model. Depending on your choice of the correlation function, the model can either honor the data (providing an exact interpolation of the data) or smooth the data (providing an inexact interpolation).<p>Depending on the number of input parameters, the number of design points, and the number of responses (outputs) of the Kriging model, the process of building the model can be very time consuming. As the size of the matrices increases, the amount of CPU power required for manipulating the matrices grows exponentially. Therefore, generating a good Kriging model that uses many design points can take a substantial amount of time even after all the data points are analyzed.


