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Moreover the identified parameters for the model are presented.
Proper modeling technique is developed/employed at each scale to efficiently encounter the identified parameters.
Some advanced measures for improving the accuracy of the identified parameters are used.
A filter is introduced to suppress the variation on the identified parameters.
Our results highlight that non-uniqueness of the identified parameters is a significant issue.
The identified parameters are exported from the DNN output layer directly.
An ellipsoidal parametric uncertainty set is constructed from the covariance matrix of the identified parameters.
The identified parameters are obtained through solving the estimation equations by simple least square method.
The identified parameters are also used to estimate the time-varying contact forces between the vehicles and the bridge.
This input uncertainty will migrate through the MNET routine, and will result in an uncertainty on the identified parameters.
Furthermore, a numerical example is given using the identified parameters for a thermomechanical coupling analysis at elevated strain rates.
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