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The mathematical models, the computational scheme, and the results corresponding to different loading scenarios are discussed.
In a number of ocean models, the computational problems resulting from the multiple time scales are addressed by splitting the fast and slow dynamics into separate subproblems that are solved by different techniques.
For spatially resolved models, the computational effort is too large to fit all models on the entire genome.
With increasing size and complexity of genome-scale models the computational effort for their analysis increased as well.
For some models, the computational resources (Intel Core i7 at 3.2 GHz with 12GB of memory) were not sufficient, and within a maximal runtime of 30 min no result was found.
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The paper presents the model, the computational process and applications.
Based on a 3-D digital model, the computational tool can determine the pattern of slits necessary to make the sheet conform to the desired shape.
We also model the computational delays as illustrated in Figure 2. Figure 2 Downlink scheduling time-line and computational delays.
Additionally, by reducing the number of features used to train a model, the computational complexity of the task is reduced.
In fully 3D CFD-based model, the computational requirements increase by several orders of magnitude.
In the proposed model, the computational complexity is given by T K =Oleft(J*K+chi_{text{max}}right).
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