Class hierarchy: Model.
The model classes are responsible for mapping variables into responses when an iterator makes a function evaluation request. There are several types of models, some supporting sub-iterators and sub-models for enabling layered and nested relationships. When sub-models are used, they may be of arbitrary type so that a variety of recursions are supported.
SimulationModel: variables are mapped into responses using a simulation-based Interface object. No sub-iterators or sub-models are used.
SurrogateModel: variables are mapped into responses using an approximation. The approximation is built and/or corrected using data from a sub-model (the truth model) and the data may be obtained using a sub-iterator (a design of experiments iterator). SurrogateModel has two derived classes: DataFitSurrModel for data fit surrogates and HierarchSurrModel for hierarchical models of varying fidelity. The relationship of the sub-iterators and sub-models is considered to be “layered” since they are not used as part of every response evaluation on the top level model, but rather used periodically in surrogate update and verification steps.
NestedModel: variables are mapped into responses using a combination of an optional Interface and a sub-iterator/sub-model pair. The relationship of the sub-iterators and sub-models is considered to be “nested” since they are used to perform a complete iterative study as part of every response evaluation on the top level model.
RecastModel: recasts the inputs and outputs of a sub-model for the purposes of variable transformations (e.g., variable scaling, transformations to standardized random variables) and problem reformulation (e.g., multi-objective optimization, response scaling, augmented Lagrangian merit functions, expected improvement).