Sentence examples for definition of models from inspiring English sources
The modelling approach focuses on the definition of models and model transformations, from the application requirements to code generation, emphasizing dynamic reconfiguration of the application.
Within the Model-Driven Engineering paradigm, software development is based on the definition of models providing different views of the system to be constructed and model transformations supporting a (semi automatic development process.
MDE experts are responsible for the definition of models and model transformations, while formal verification experts conduct the verification process.
The results may guide the definition of models and the selection of the size of representative volume elements in sequential multiscale models of fiber networks.
The proposed approach is based on the concept of impedance boundary conditions and is applied in conjunction with a Dual-BEM approach, thereby allowing the definition of models in which only very compact descriptions of the propagation domain are required.
The problem is solved by developing and implementing a 3D Boundary Element Method formulation using a normal derivative integral equation (TBEM), thereby allowing the definition of models in which only the discretization of the barriers as single open surfaces is required.
The definition of model transformations is usually based on meta-models describing the abstract syntax of languages.
A pragmatic definition of model completeness is given, based on situated interpretation of meaning and theory of communication.
The difference between forward- and backward-looking models is also explained, as are issues relating to the appropriate definition of modelling languages in general based on representation theory.
A range of sources have been adopted in the definition of model components whose validity is assessed against case studies presented in literature.
Major steps in the population derivations include data analysis, reference-population construction, definition of model parameters in terms of reference populations, linking model parameters with data, seeking best estimates for the model parameters, uncertainty analysis, and assessment of the outcomes.
