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Using model predictions to guide an experimental program.
While computational methods have advanced significantly, relatively few studies have directly compared model predictions to experimental data.
Favourable comparison of the resulting model predictions to data (Fig. S11 and Table S9 in the Supplement) confirms the validity of this approach.
We compared model predictions to experimentally measured AF properties and performed parametric studies.
A comparison of model predictions to the results of the licensing analyses shows reasonable agreement.
This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations.
We also compare DNN model predictions to microgravity experiments for the same alloy.
The validity of model outputs was assessed by comparing model predictions to the experimental results from mechanical COF testing.
By detailed comparisons of model predictions to experimental data the performance of the new model is demonstrated.
Corroboration involved comparing model predictions to four well-monitored contrasting habitat sites within the Maurepas Basin, Louisiana, USA.
By using this scheme, it is possible to tune the model predictions to match with the monitored responses.
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