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Rather the construction of surrogates.
By addressing this issue, it is shown that this information is crucial for the reliable construction of surrogates through computations.
The computational cost of the high-fidelity meso-scale simulations is a challenge in construction of surrogates in such hierarchical multi-scale frameworks.
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The algorithm relies on the construction of surrogate models to replace the original model within the region of interest.
Considered the uncertainties in the components and physicochemical properties of the actual diesel fuel, it is believed that the construction of surrogate fuel has only reasonable solutions and there is no optimal solution.
The results of the 1-D model enable the construction of surrogate functions for the thermal resistance and the pressure drop of the heat sink, which are considered as the objective functions for the multi-objective optimization through a genetic algorithm that leads to the optimal geometric parameters.
Since the construction of such surrogates can only be performed on the complete spike train, this measure could not be sensibly applied to the subsets of spikes in our analysis (i.e., ISO, CC, UE, as well as Lo and Hi used in the amplitude analysis).
The construction of a surrogate model requires evaluation of the original model to gather the data necessary for building the surrogate.
The methodology is based on the construction of a surrogate model using artificial neural-networks.
Sampling requirement is also dramatically low for the construction of these surrogate models.
This renders a computationally efficient construction of the surrogate whose form is determined using a recursive relationship from the solution of the transport equation.
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