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The material properties of the model are tuned to match the experimental natural frequencies measured in free air.
The parameters of the ARIMA model are tuned so that the mean square error (MSE) of the forecaster is minimized.
In the second phase, the parameters of the model are tuned via the training of a neural network through backpropagation.
The mechanical properties of the beam FE spine model are tuned so that its deformation behaviour is very similar to that of the offline solid spine model.
In this approach, a relative permeability and capillary pressure model are tuned in an optimization procedure to reach an acceptable match between the laboratory results of the fluid production, pressure drop and the corresponding values calculated by the model.
However, the quality of the core field modeling results when applied to real data can only be assessed if the data selection parameters, the external field model parametrization and the constraints applied to the model are tuned by the "scientist in the loop".
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The fluid equation of state (EOS) model was tuned on the production PVT data.
Only when the model is tuned to the experimental data they are successful.
This model was tuned against experimental data on three different catalysts at different temperatures.
Kinetic parameters of five lump model were tuned with industrial data.
The model is tuned to be similar to that of Honda (2009), and it covers the region.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com