Sentence examples for modelling complexity and from inspiring English sources

These aspects are quite often neglected in design and assessment both with linear and nonlinear models due to the modelling complexity and inherent increase in computational effort.

It is demonstrated how a domain specific language can be used to abstract the financial platform into a data stream based information model to reduce policy modelling complexity and deployment latencies through an innovative policy mapping language usable by both expert and non-expert users.

The committee advocates that the fundamental requirements are strengthened that includes transparency, uniformity in modelling complexity, and the verifiability of the probabilistic modeling.

Although there is considerable research on the experimental testing of foil-air bearing (FAB) rotor systems, only a small fraction has been correlated with simulations from a full nonlinear model that links the rotor, air film and foil domains, due to modelling complexity and computational burden.

Where this discussion goes beyond previous treatments of this topic [ 5, 11, 17] is in offering a specific taxonomy of approaches, with attendant implications for modelling complexity and for development of systematic review methodology.

However, owing to the limits imposed by model complexity and the available data, as with previous modelling studies, it was not possible to explicitly account for all of the known heterogeneity in the cervical cancer disease process.

Otherwise the requirements in terms of modelling complexity, retrospective data-mining sophistication, and amounts of data necessary for parameter estimation, will become even more staggering.

This paper proposes cost effective design methods which provide accurate prediction with minimum increase in modelling complexity through simplified Finite Element Analysis models.

In the case of neurotrophic factors, an additional level of modelling complexity is added by the fact that they are secreted, and will affect multiple cells, including non-transduced ones.

However, the modelling complexities are formidable and involve numerous steps.

Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance.