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The tuning process then finds parameter values that, when used in the model, achieve results that are as close as possible to the empirical data.
The underlying problem can be succinctly formulated in the following way: given is a numeric model, develop an efficient way of forming granular input variables so that the corresponding granular outputs of the model achieve the highest level of specificity.
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Amongst other things, this lists the miles per gallon (mpg) the model achieves when tested on the Environmental Protection Agency's simulated city and highway driving cycles.
The model achieved mean absolute percentage errors ranging from 3%to6%6% for the day ahead prediction.
The model achieved reasonable prediction (R2 = 0.939; Figure 27), but some inaccuracies occur.
The model achieved a mean square error ranging from 5.7% to 15.33%.
Results obtained through simulation show that the model achieves a high degree of accuracy.
In contrast, when the period is appropriately shorter, as shown in the figure, the model achieves dynamic transition.
The model achieved a correlation coefficient greater than 98% for sunny days and less than 95% for cloudy days.
The model achieves a good level of accuracy by carefully modeling calcine production, crane dispatching, and equipment delays.
The model achieves good agreement with flow stress strain evolution and yield data collected over many studies.
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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