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Generally, in order to evaluate the average user performance, the simulation results are collected from 20 drops and averaging over 500 loops within each drop.
To verify the feasibility that the larger N can get the better MSE performance, the simulation is given in Fig. 16, where P=N, M=⌈0.85×N⌉, K=⌈0.1×N⌉, α=0.9, N=128, N=192, and N=256 are, respectively, considered.
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As the Gillespie level still integrates time much faster than the particle level, this performance loss is, however, not relevant for the overall performance of the simulation.
Performance of the simulation procedure is illustrated in Figure 5.
Fig. 7 Model performance on the simulation data.
A comparison with experiments underlines the performance of the simulation.
Finally we will look at the runtime performance of the simulation.
The measured performance of the simulation shows perfect agreement with the estimate.
An execution-monitoring tool provides visualization and monitoring the system performance during the simulation.
Only those parameters that have demonstrated an impact on performance during the simulation studies are considered.
The data assimilation should be important to improve the performance of the simulation as well as estimate the diffusion coefficients.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com