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Gaussian process modeling (also known as kriging) is an empirical modeling approach that has been widely applied in engineering for the approximation of deterministic functions, due to its flexibility and ability to interpolate observed data.
We describe a simple empirical modeling approach for determining least-disturbed conditions for the great rivers of the Upper Mississippi River basin: Missouri, Upper Mississippi, and Ohio Rivers.
The empirical modeling approach was found to be more efficient, simpler and led to better results than those obtained with the phenomenological model approach.
Kriging is an empirical modeling approach that has been widely applied in engineering for the approximation of deterministic functions, due its flexibility and ability to interpolate observed data.
Additionally, an empirical modeling approach on the bond strength degradation of rebars at elevated temperatures was proposed and it was found that comparison results have quite promising consistency based on the modeling process.
Besides the impedance-based part of the model, the influences of the typical hysteresis effect of NiMH batteries is described in detail and an empirical modeling approach is introduced.
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The proposed modeling method takes advantages of both physical and empirical modeling approaches, can accurately predict the transient behaviors in real-time and significantly reduce the computational burden.
Using such a combination of physical and empirical modeling approaches, we seek to decode the "glass genome," enabling the improved and accelerated design of new glassy materials.
Based on the above analysis, an empirical predicted model for the PEM stack has been developed by the combination of mechanistic and empirical modeling approaches to characterize and predict the voltage current characteristics without examining in depth all physical/chemical phenomena.
As mechanistic models become further advanced and gain traction in the coming years, we also expect that empirical modeling approaches will remain relevant in the safety space.
Although systematic information on nitrate levels in groundwater in other parts of the world is more limited, empirical modeling approaches have indicated that users of shallow wells in areas with high nitrogen inputs, well-drained soils, and unconsolidated rocks are most at risk of consuming high-nitrate groundwater (Nolan et al. 2002).
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