A metamodel approximates the behavior of a more complex model. A
common and superficially attractive way to develop a metamodel is
to generate large-model data and use off-the-shelf statistical
methods without attempting to understand the model's internal
workings. This report describes research illuminating why it can be
important to improve the quality of metamodels by using even modest
phenomenological knowledge to help structure them. The work helps
to understand multiresolution, multiperspective modeling.
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