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A functional model validation procedure is proposed to assess whether the intended modeling purpose indeed represents a relevant proposal and whether the model represents the system behavior sufficiently well.
We propose a model validation procedure that consists of a prediction error identification experiment with a full order model.
In the model validation procedure, a satisfactory comparison between the experimental and computational results of the primary and secondary mass flow rates was obtained for both flow regimes.
A systematic model validation procedure including uncertainty quantification, model update and prediction is described based on a non-probabilistic interval model.
Numerical results are included based on laboratory data to show typical steps of a model validation procedure, i.e., the interpretation of confidence intervals, the compliance with physical laws, the identification and elimination of redundant model parameters, the computation of optimum experimental designs and the identifiability of parameters by determining weight distributions.
As we recently showed, 6 predictive modelling can select active regions in the catalyst space, provided that the following three conditions are met: first, you need sufficient experimental data for building a predictive model; second, you should have acces to a large number of diverse ligand-metal complexes; and, finally, a robust model validation procedure must be available.
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I do numerical modelling of physical phenomena for a living, so I know something about model validation procedures.
As there are few studies that allow empirical observation of "true" coarse root C given the time and effort involved in destructively sampling belowground components, model validation procedures are limited for assessments at national scales.
A procedural practice guideline was derived from a discussion of the underlying models in order to propose beneficial design options concerning regional sampling (i.e., number and size of observed areas), waste stream definition and investigation, selection of independent variables and model validation procedures.
Full statistical notations of Bayesian geostatistical modelling, PAF and model validation procedures are presented in the technical appendix.
Multivariate logistic regression analyses and model validation procedures were carried out in SAS version 9.1 (SAS Institute, Cary, NC) and R version 2.9 (The R Foundation for Statistical Computing, Vienna, Austria).
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