Training an Approximation with Flow Adapter Factors

You can train an approximation using parameters from a DOE adapter or Monte Carlo adapter.

Before you begin: Configure one of the following flow adapters:
  • DOE
  • Monte Carlo

For more information, see Configuring the DOE Adapter or Configuring a Monte Carlo Simulation.

See Also
About Approximation Training
Approximation Adapter
  1. Expand the activity that contains the flow adapter.
  2. From the Utilities section of the action bar, click Approximation Trainer and drop it to the right of the flow adapter in the same activity.
  3. Double-click Approximation Trainer .
    The Approximation Trainer Editor opens.
  4. Click the Configure Training tab.
  5. Enter a descriptive Approximation Object Name.
  6. Add a new configuration:

    Training an approximation requires the definition of at least one configuration. A configuration specifies an algorithm type, algorithm hyperparameters, validation type, and validation hyperparameters for a specified set of output parameters. Each output parameter can be included in only one configuration, and each configuration must include at least one output parameter.

    1. Click Add Configuration.
    2. From the Algorithm tab, configure the Approximation Type, along with any hyperparameters relevant to that type, such as a polynomial order for a Response Surface Model approximation.
    3. From the Validation tab, configure the Validation Type and hyperparameters.
    4. From the Outputs tab, select one or more output parameters to assign them to the configuration. If all parameters assigned to a configuration are reassigned to other configurations, the empty configuration is deleted. If you are using factors from a flow adapter as parameters, they will appear here.
    5. Click OK.
      The new configuration is created.
  7. Click OK.

The Approximation Trainer adapter creates an approximation object when it runs. You can use the approximation object to predict output parameters with the Approximation adapter.