Categories: Optimisation, Surrogate Modeling
State: ❌ must be refactored to work with the new version of the code (see Issue #147)
SMUse is a module to use a surrogate model to "predict" new data that could be used in a CEASIOMpy workflow.
SMUse takes as input a CPACS file, it must contained a path to a trained surrogate model in it.
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.
SMUse should output the same results as the original modules it replaces.
SMUse is a native CEASIOMpy module, hence it is available and installed by default. It is base on the SMT: Surrogate Modeling Toolbox