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Predictivity analysis suggests good generalization of the model development methodology through robustness to dataset sampling in cross-validation.
This paper proposes a SPICE model development methodology for quantum-dot cellular automata (QCA) cells and presents a SPICE model for QCA cells.
Model development methodology comprises an integrated preliminary cost estimation system (IPCES), and complex quality functions and deployment of a multi-criteria decision making (MCDM) approach.
While the general yield criterion is developed as an extension of a specific non-quadratic anisotropic yield criterion, the same model development methodology can be applied to other novel or existing yield criteria that are defined in the deviatoric stress space.
Figure 3 Model development methodology flow chart.
Fig. 1 Overall Model Development Methodology.
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Short time-to-market, easy testing and error handling, separating concerns, improving reusability and extendibility of obtained models in similar applications are some benefits of the model driven development methodology.
PG performed the simulations, data synthesis and interpretation and wrote the manuscript, WMP provided assistance in model development and methodology, KH provided regional GCM outputs.
They are organized in topical sections on concurrency, applications of formal methods to new areas, quantity and probability, formal verification, modeling and development methodology, temporal logics, abstraction and refinement, tools, as well as testing and runtime verification.
The methodology in Chapter 2, Model development and validation methodology: a classical big data application, is used to evaluate and rank the models indicating which six models are recommended for future use.
Ensuring consistency and correctness of models across the various design steps is crucial in model-based development methodologies.
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