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In this paper a practical procedure is proposed to overcome technical problems of structure control due to a large model size.
Considering two characteristic lengths of a crystal, the standard discretization of such a system (by, e.g. finite differences) leads to quite a large model size, which may be unsuited for model-based control and parameter identification purposes.
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Stepwise BIC selects models of size 13 (BE) and 11 (FS), since BIC generally favors smaller models by imposing a larger model size penalty.
Given the large model size, it is important to determine a suitable number of autoregressive lags and which time-lagged values to include in the model.
Specifically, (1) we have characterized 33 shape classes by edge number, detection difficulty, and observed frequency (2) our results support the claim that model architecture directly influences detection difficulty, and (3) we found that GAMETES will generate a maximally diverse set of models with a variable population prevalence and a larger model population size.
Even larger model sizes can also be handled if the computer random-access memory (RAM) and the compiler used support this.
Multi-GPU systems facilitate the development of accelerated codes and enable us to investigate larger model sizes and/or more detailed computational domain resolutions.
SPCA tends to select a larger model as represented by its average model size and high FPR.
For a MD simulation, the larger the model size, the less obvious the boundary effect on the simulation results.
However, for an off-line predictive system with multiple large models, the size of models can become an issue, as they need to be downloaded and used on a local computer.
With a sample size of 50, the study was powered to detect, with up to three predictors in a model, a large effect size (f = 0.33) at the 5% level with 90% power.
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CEO of Professional Science Editing for Scientists @ prosciediting.com