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The result showed that the gPC method reduces computational effort for uncertainty quantification of complex chemical processes with an acceptable accuracy.
The developed method reduces computational burden over communication networks.
On the other hand, the ED method reduces computational cost but provides lower quality meshes.
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The combination of these two methods reduces computational time and improves model fit [ 65].
These methods reduce computational time and estimate the network by a heuristic.
The analytical method significantly reduces computational time and is suitable for direct engineering application.
The results of our case studies confirm that the method substantially reduces computational effort while maintaining accuracy.
This type of methods significantly reduces computational efforts for large systems; however, the uncertainty of convergence is a major concern.
Thus by searching for risk variants in a haplotype instead of individual SNPs, the proposed method (SCARVA) significantly reduces computational burden several fold depending on the size of the analysis dataset.
Moreover, we demonstrated that the proposed method reduces the computational cost of the simulation.
A comparison to the conventional construction method shows that DRSQP method reduces the computational costs required for P-box constructions.
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