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Substantial computation time savings are demonstrated, which allow a fixed error in three studied system functionals to be achieved with an order-of-magnitude reduction in computation time.
The substantial computation time may be reduced by using statistical learning techniques to develop surrogate models, or metamodels, which efficiently approximate the complex and implicit relationship between predictor variables, such as bridge design and ground motion intensity parameters, and the predicted bridge component seismic responses (e.g., column and bearing deformations).
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This requires substantial computation times, up to 6 hours per scan on a dedicated server, to reach the required precision.
It involves significant computation time, which restricts its practical usefulness.
In order to reduce this substantial increase in computation time, the HRI analysis in PHRI is reduced by computing T values only for the regions identified as having PSPs (Fig. 7).
The results show that the combined genetic algorithm-subregion method has good convergence with a substantial reduction of computation time.
A substantial reduction of computation time is achieved by enabling separate time advances of background and beam particle species in the time-averaged fields.
A substantial gain in computation time is obtained this way compared to a finite difference evaluation of sensitivity, which requires solving a second nonlinear system for each design variable.
Most currently available structural variant callers employ multiple passes through the entire input file, requiring substantial memory and computation time.
However, a very large number of permutations and a substantial amount of computation time are required to accurately calculate the P-value to test for differential expression.
A substantial gain in computation time is essential for a successful application of resampling techniques to the validation of gene expression profiling rules.
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