Configuring Universal Kriging Model Technique Options

Universal Kriging approximation is an interpolation technique. Universal Kriging approximations are extremely flexible because you can choose from a wide range of correlation functions to build the meta model. In addition, depending on the correlation function that you choose, the meta model can either “honor the data,” providing an exact interpolation of the data, or “smooth the data,” providing an inexact interpolation.

For more information, see The Universal Kriging Model.

  1. Double-click the Approximation component .
    The Approximation Component Editor appears.
  2. From the Approximation Component Editor, click the Technique Options tab.
  3. Select the Correlation Function. The correlation functions interpolate the data points exactly.

    The following options are available:

    OptionDescription
    Gaussian You can use the Gaussian correlation function for approximating smooth functions. However, it produces a poor fit when sampling points are too close.
    Exponential If the sample points are close, use the Exponential correlation function.
    Cubic Spline You can use the Cubic Spline correlation function to correlate data that does follow a specific pattern. The Cubic Spline correlation function is more accurate than linear interpolation and provides a smooth interpolant.
    Matern Linear You can use the Matern Linear correlation function if the Gaussian and Matern Cubic correlation functions produced an unacceptable fit. The Matern Linear correlation is more robust, but less accurate, than the Matern Cubic correlation function.
    Matern Cubic You can use the Matern Cubic correlation function if the Gaussian correlation function produced an unacceptable fit. Typically, the Matern Cubic correlation function is more accurate than the Matern Linear correlation function.
  4. Enter a value for the smoothing parameter, Alpha (0⁢ ≤ alfa ≤ 0.1⁢).

    Isight 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.

  5. Enter a value for the Minimum distance between points.

    Occasionally, when points are clustered together, the matrices used in fitting the Universal 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. Isight uses other numerical techniques internally to improve the performance and robustness of the approximation.

  6. Select the Input Parameter Scaling to specify the method used to standardize the range of the input parameters.
    OptionDescription
    Min-Max Normalization (default) Normalize the input parameter values between zero and one.
    Mean Zero Standardization Rescale the input parameter values to a mean of zero with unit variance.
  7. Click OK to save your changes and to close the Approximation Component Editor.