The dynamics modeling process is divided into the modeling of multi-attribute parts, system modeling based on assembly features, dynamics modeling and system implementation stage from the analysis process.
The next step in the static reservoir modeling process is the stratigraphic modeling which is one of the most important aspects, because its result is very vital to the overall accuracy of field development.
Generally, dynamic criteria can be incorporated in signal-oriented models from control engineering, but the modeling process is time-consuming, knowledge-intensive and the reusability of models is limited.
The overall goal of the modeling process is to develop systems-level models that are sufficiently flexible to be used on a variety of different applications.
An essential part of the QSAR modeling process is the interpretation of the model in terms of structural variations leading to corresponding changes in the biological activity.
Since the building of the model is dramatically complicated and cumbersome, in this section modeling process is described briefly and shown in Fig. 11.
In order to obtain a fast and easy to use tool, something that is necessary for an analytical model to be chosen over finite elements and empirical formulas, modeling process is developed within the framework of two-dimensional elasticity.
Generally, the modeling process is initiated by the development of a conceptual model followed by the development of a mathematical model.
Using the OPCAT modeling environment (downloadable from www.opcat.com/downloads/restricted), the first step in an OPM-based modeling process is to determine and phrase the name of the main process being modeled.
An extra set of test results which is not involved in the modeling process is employed to verify the applicability of the proposed model.
Because the modeling process is exploratory, the inference criterion used for comparing the models under consideration is their ability to fit the observed data; i.e. models are compared directly through their minimized minus log likelihood.
