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In order to gain computation time in the optimization algorithm the present ANN approach is applied to the responses of the flow solver to the centerbulb design parameter changes.
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It follows a non negligible gain in computation time especially when the post-buckling behavior is performed.
This exercise reveals the gain in computation time associated with the use of a low-dimensional model (by a factor of 6).
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.
The gain in computation time will be given in Section 5.2.
A substantial gain in computation time is essential for a successful application of resampling techniques to the validation of gene expression profiling rules.
In some cases, SLM and the Louvain algorithms show significant gains in computation time.
We felt the precision gained in 50 iterations compared to 25 was of value, and that the extra precision gained in 100 iterations compared to 50 was insufficient to warrant the observed modest increase in computation time.
Simulations show significant gain in terms of computation time and iteration count in comparison with results obtained with preconditioners based on incomplete LU (ILU) factorization.
The experiments performed on a 6-node Hadoop cluster proves that the proposed approach outperforms A* algorithm and achieves significant gain in terms of computation time.
It leads to a high-performance simulation, combining accuracy (global simulation error less than 1 dBW) and small computation time (gain of about 10,000 times compared to a full deterministic reference model).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com