A reduced-order model makes its output available to the calling model in the form of variables and operators. You can define output variables in the Model Reduction study step before training the reduced-order model, specifying an output variable base name and a corresponding global expression valid in the unreduced model. Variables defined in this way become available as global variables in a calling model, but with a namespace prepended to indicate which reduced-order model instance they belong to. For example, an output variable defined as g in the Model Reduction study step will be rom1.g in the caller if the reduced-order model feature name is rom1.

Some types of reduced-order models define operators with fixed base names, but using the reduced-order model feature namespace. For example, reduced-order models that are capable of reconstructing the complete solution vector define <rom>.eval(<expr>) which evaluates the expression <expr> using the solution reconstructed for the current values of the reduced-order model’s inputs.