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At present, models predicting shell multistability are often characterised by a compromise between computational efficiency and result accuracy.
The focus of this paper is to explore several low-order beam finite elements that offer the best compromise between computational efficiency and accuracy.
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Therefore, a trade-off between computational efficiency and handling of multiple association hypotheses is needed.
However, this stepwise approach has a trade-off between computational efficiency and detection of optimal gene-gene interactions.
Hybrid algorithms provide a compromise between the computational efficiency of a coarse-scale model and the representational accuracy of a fine-scale description.
Ligmann-Zielinska (2013) also proposed a model-independent variance-based GSA, which obviates the assumptions of model linearity and offers an acceptable compromise in computational efficiency.
Our feeling is that simple mixture models, based on an array of a fixed, and low, number of pre-specified empirical amino-acid profiles will be the best compromise between robustness and computational efficiency.
We studied an appropriate reconstruction algorithm searching for the best compromise between computational complexity and high precision.
The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision.
For the PML, the influence of its main parameters on the global error is investigated to make a compromise between computational costs and maximum error.
However, because it would require enormous computational resources, one should find a compromise between computational cost and sampling breadth.
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