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An alternative approach based on conceptual modelling for creating surrogate models is presented.
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A workflow is presented to create surrogate models from any or all of the properties which affect production.
This algorithm exploits stochastic expansions derived from the Non-Intrusive Polynomial Chaos (NIPC) technique to create surrogate models utilized in the optimization process.
The Poisson simulation creates surrogate traces by stimulating a single model neuron with synaptic inputs.
In addition, according to two different penalty functions, two subspaces are created to construct local surrogate models and speed up the local search.
The optimization process involves both multi-point space mapping, and mixed modeling techniques that utilize space mapping to create a physics-based surrogate model.
Asynchronous parallel algorithms for finding minima fast by fitting functions to surrogate models.
Many researchers recommended to use more complicated surrogate models.
In the surrogate framework, several surrogate models are validated and the PWS (Predicted residual sum of square-based Weighted average Surrogate) model is chosen for our further investigation.
To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models.
In this context, they are called surrogate models or metamodels.
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