CEASIOMpy
SMUse
Categories: Optimisation, Surrogate Modeling
SMUse is a module to use a surrogate model to “predict” new data that could be used in a CEASIOMpy workflow.
Inputs
SMUse takes as input a CPACS file.
If SMTrain has not be called before in the workflow, then you must specify in CEASIOMpy’s GUI a path to a trained surrogate model in it.
Analyses
SMUse uses a trained surrogate model to replace a module in the CEASIOMpy workflow. The aim is to use train the surrogate model upstream and be able to get quick results from the surrogate model in a new workflow.
Outputs
SMUse should output the same results as the original modules it replaces.
Installation or requirements
SMUse is a native CEASIOMpy module, hence it is available and installed by default.