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In addition, the designed controller also presents appreciable robustness to model error and determination error.
Reliability analysis includes model error and other random problem parameters like steel and concrete strengths, and dead and live loads.
It is found that the extrapolation uncertainty contains two sources of error, namely model error and random error.
However, understanding model output is not limited to interpreting complex dynamics; scientists must also contend with the possibility of model error and uncertainty.
Original contributions include novel metrics and visualization techniques for understanding model error and a new robustness framework that accounts for variability in model comparison.
As autonomous teleoperation control becomes the new hotspot for space teleoperation, interesting control challenges including large variant timedelay, model error and various disturbances are presented.
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The model errors and computational aspects are analysed and discussed.
This leads to a robustness of the identification procedure against unknown measurement errors, unknown model errors and unknown model orders.
The chapter provides select model equations as well as quantitative assessments of model errors and judgments of model suitability.
Results from a unique laboratory test case with strong anisotropy indicate that porosity model errors are smaller than structural model errors, and that porosity model errors in both depth and velocity are substantially smaller for anisotropic versus isotropic porosity models.
Results show that uncertainty contributions of the model errors and interpolation errors are similar in terms of entropy H′ between land evaluation maps (fairly low H′ of 0.47 and 0.38 for model and interpolation error respectively).
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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