Sentence examples for model prediction uncertainty from inspiring English sources

In addition, the proposed method explicitly considers surrogate model prediction uncertainty.

For this highly non-stationary setting we quantify the groundwater flow and transport model prediction uncertainty for various levels of hydrogeological uncertainty.

In this introductory paper to the special issue on crop model prediction uncertainty, we present and compare the methodological choices in the studies included in this issue, and highlight some remaining challenges.

And the space design of operation variables are proposed to calculate directly from the inverse model with the effect of confidence interval when the model prediction uncertainty are considered.

The uncertainty analyses show that fracture spacing is the most important model parameter and model prediction uncertainty is relatively higher at the interface between the preferred flow path and its parent bedrock.

In this paper, the model prediction uncertainty is evaluated for a model describing the continuous drying of single pharmaceutical wet granules in a six-segmented fluidized bed drying unit, which is part of the full continuous from-powder-to-tablet manufacturing line (Consigma™, GEA Pharma Systems).

The separate effects on model prediction uncertainties due to parameter estimation and measurement errors were studied.

However, model prediction uncertainties on discharge only increase significantly for very coarse spatial discretizations.

This indicates that many well-known impedance control phenomena naturally emerge from the first principles of a stochastic optimization process that minimizes for internal model prediction uncertainties, along with energy and accuracy demands.

We can identify at least two other potential sources of error in the model predictions: uncertainty in the gas phase composition (saturation of SA) and particle-phase processes (e.g., SA dissociation, condensed-phase impurities, and chemical reactions).

In the case when the internal model is learned from a plant with stochastic characteristics similar to the extended SDN model, the prediction uncertainty reflects the limb's underlying noise-impedance characteristics, i.e., the fact that co-contraction reduces variability.