Discrete event simulation (DES) modeling has several advantages over simpler modeling techniques in health economics, such as increased flexibility and the ability to model complex systems.
These approaches might be suitable using simpler modeling techniques such as the Cobb-Douglas production function, which, however, is not suitable here as we show in "Data description and model selection" section.
The seismic performance of the SC-SPSWs with beam-only-connected and fully-connected web plates is compared using results of nonlinear response history analyses in which relatively simple, yet conservative, modeling techniques are employed.
The available heterogeneous object modeling techniques model simple material-distributions only and just a few of them are capable of modeling heterogeneous objects with complex geometries.
There are a variety of spatial modeling techniques, which include simple regression methods, such as spatially auto-correlated principal components analysis [ 54], and random forests [ 55, 56] that are used to evaluate environmental and biodiversity associations.
The results show that simple turbulence modelling techniques are sufficient to evaluate the performance of the turbine in the designed operating conditions and can predict when the turbine will run outside each parameter's operational range.
We add an additional MoC, a discrete-event (DE) model of the environment in which the traffic light operates, including a simple fault model, yielding a model that combines three different modeling techniques.
Data modeling techniques have evolved from the use of simple file systems to relational, object, and object-relational models.
It is shown that using a simple particle deposition modeling technique, together with a tip-sample convolution algorithm, one can get relatively easily estimates of uncertainty components related to data processing methods in nanoparticle analysis.
Numerous experimental results show that the proposed two-step approach outperforms simpler application strategies of surrogate modelling techniques, getting comparable performances to approaches based on electromagnetic simulation but with orders of magnitude less computational effort.
