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Force-deformation curves of real soft tissue samples were obtained experimentally, and the model was tuned accordingly.
This model was tuned against experimental data on three different catalysts at different temperatures.
The learning rate (contribution of successive trees to the growing model) or shrinkage for each model was tuned so that no less than 1,000 trees (or iterations) were included in the final model (following methods outlined in29).
The wear model was tuned using experimental measurements and was then able to accurately predict the volumetric polyethylene wear volume during experiments with different kinematic inputs.
After matching water and polymer flooding results, the surfactant simulation model was tuned through history matching the performance of a series of SP corefloods.
The parameters of a finite element model with smeared crack material model was tuned based on the stress-strain relationship of LSHCC measured from the tensile tests in this study.
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Only when the model is tuned to the experimental data they are successful.
Kinetic parameters of five lump model were tuned with industrial data.
A thermodynamic model of engine cycle is developed in AVL Boost®; the model is tuned and validated using experimental data.
As an example simulation, the model is tuned to define the dynamic response characteristics of tires rolling on bumpy surfaces.
In the second phase, the parameters of the model are tuned via the training of a neural network through backpropagation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com