Creating a User-Defined Approximation Using the Kriging Model Technique

You can create a user-defined approximation based on Kriging approximations, which 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.

Important: If you are selecting a component that is in a Task Plan, you must select the component using the model explorer.

Related Topics
Kriging Model

  1. Do one of the following:

    • From the Design Gateway,

      1. Select the component for which you want to create an approximation:
        • Select a component on the Sim-flow tab or in the model explorer, and click the Approximations button on the component title bar.
        • Right-click the component on the Sim-flow tab or in the model explorer, and select Approximations.

          The Approximations dialog box appears.

      2. On the right side of the dialog box, click New.

        The Approximation Wizard appears.

    • From the Runtime Gateway,

      1. Select a component on the Sim-flow tab or in the model explorer.
      2. Click the Visual Design tab, and click the button on the component title bar.

        The Approximation Wizard appears.

  2. In the Name of approximation text box, enter a name for the approximation.

  3. Click User Defined, and click Next.

    The Approximation Technique screen appears.

  4. In the Approximation technique list, select Kriging Model.

  5. Click Next.

    The Input and Output Parameters screen appears.

  6. Determine which parameters you want to use for your approximation by selecting the corresponding check boxes in the first column. Alternatively, you can click Check to add all the selected parameters. To clear all the parameters, click Uncheck.

    If your parameters contains arrays, click the check box next to the array root to select all members of the array.

  7. Click Next.

    The Kriging Technique Options screen appears.

  8. Select the Fit Type.

    Option Description
    Anisotropic Select this fit type if the independent variables represent different physical measures (e.g., time, distance, velocity, etc.) or when the independent variables have different scales. Anisotropic fit is the general case for ordinary Kriging when every independent variable behaves differently.
    Isotropic Select this fit type if all the independent variables behave similarly. Isight handles all θ k values as if they are identical. Typically, the Isotropic fit is faster than the Anisotropic fit because Isight searches for only one optimum theta value.

  9. Select the Correlation Function. The correlation functions interpolate the data points exactly:

    Option Description
    Gaussian Select this option to approximate smooth functions. However, the Gaussian correlation function can produce a poor fit when sampling points are too close.
    Exponential Select this option if the sample points are close.
    Matern Linear Select this option 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 Select this option if the Gaussian correlation function produced an unacceptable fit. Typically, the Matern Cubic correlation function is more accurate than the Matern Linear correlation function.

  10. Enter the Filter Distance.

    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 a value called the Filter Distance are removed from the sample set before fitting. Isight uses other numerical techniques internally to improve the performance and robustness of the approximation.

    Note: When the number of points is large, Kriging automatically filters out points that are clustered until only 500 points remain even if the Filter Distance value is zero. Isight builds the Kriging model using these 500 points.

  11. Enter the Maximum Iterations to Fit.

    Isight uses an iterative procedure to fit a Kriging model with the maximum likelihood estimate. Isight limits the maximum number of iterations used to fit a model for each response based on this value.

  12. Click Next.

    The Sampling Options screen appears.

  13. Select the desired sampling method (Random Points, Data File, or DOE Matrix), and configure the corresponding options as described in Configuring the Random Points Sampling Method, Configuring the Data File Sampling Method, or Configuring the DOE Matrix Sampling Method.

    If the model has already executed and you had already created an approximation for the selected component, you can select Component History Data. Isight uses the history data of the selected component for the approximation and bypasses the sampling range option.

  14. Click Next.

    The Sampling Range screen appears

  15. Select one of the following:

    • Absolute Values. This option defines the region by using absolute bounds for each inputs parameter. You need to specify the Lower and Upper values for each parameter in the corresponding columns.

    • Relative to Baseline. This option defines the region by applying relative move limits to the baseline values in both directions. You need to specify the baseline, move limit percentage, and minimum move limit for each parameter in the corresponding columns.

  16. Click Next.

    The Error Analysis Method screen appears.

  17. Select the desired error analysis method for the approximation:

    • Separate data set. This method compares exact and approximate output values for each data point of the second (additional) set.

      1. Click Next.
      2. Select the desired sampling method (Random Points, Data File, or DOE Matrix), and configure the corresponding options as described in Configuring the Random Points Sampling Method, Configuring the Data File Sampling Method, or Configuring the DOE Matrix Sampling Method.

    • Cross-validation. This method selects a subset of points from the main data set, removes each point one at a time, recalculates coefficients, and compares exact and approximate output values at each removed point.

      1. In the first text box, type the number of points from the total number of sampling points that you want to use for cross-validation error analysis.
      2. Click Use a fixed random seed for selecting points and specify a seed value to use for the random number generator when determining the set of sample points selected for cross-validation. This option allows you to reproduce the approximation with the same set of points later, if desired.

      For more information about cross-validation, see About Cross-Validation.

    • No error analysis.

  18. Click Next.

    If you are performing an error analysis, the Approximation Improvement Options screen appears; if you choose to skip the error analysis, the Runtime Options screen appears.

  19. If you are performing an error analysis, you can configure the approximation improvement options.

    For more information, see Improving Approximations using Sequential Sampling.

    You can choose to improve the approximation by allowing Isight to add sample points. Isight will use a sequential sampling technique that is appropriate for the approximation technique selected.

    1. Enter a target for the average prediction error of the approximation. Isight will add sample points sequentially until the average error falls below the value that you enter. Valid values are 0.0–1.0.
    2. Enter the maximum number of additional points that you want to use for improving the approximation.
    3. Enter the maximum number of iterations to improve the approximation. Dividing the maximum number of additional points by the maximum number of iterations results in the number of points that are added during each iteration of sequential sampling. Although it is desirable to add one point during each iteration, choosing fewer iterations reduces the time taken to fit the approximation model.
    4. Click Next.

      The Runtime Options screens appears.

  20. Set the Store coefficient data in file parameter named option. When activated, this option creates a file parameter that stores the approximation’s coefficient data. This option is useful if the approximation is initialized or updated (re-initialized) during execution and the coefficient data are needed for custom postprocessing. It is also useful if you want the coefficient data preserved in your database. For more information on file parameters, see Using File Parameters.

  21. Click Finish.

    A message appears prompting you to initialize the approximation.

  22. Perform one of the following actions: