First, we construct a simple theoretical model of the dependence of the calculation cost on parameters of the integration scheme such as N, (Delta t_{mathrm{soft}}), θ and (r_{mathrm{cut}}).
Since the cost of parameter estimation is usually related to the number of receiving antenna and the oversampling factor, the result also implies that the cost of parameter estimation can be decreased when the smoothing factor is properly selected.
This paper presents a methodology to combine experimental, simulation, and statistical tools to reduce the time and cost of parameter studies.
However, such a synchronous calculation method is very time-consuming for the simulation of fluid flow in porous media owing to the large computation cost of parameter passing between MD and LBM.
However, we found that in our context this does not provide any clear advantages, since the computational cost of parameter- and dynamic analysis that go beyond qualitative aspects is already close to that of an ODE model while still incorporating many abstractions of the underlying mechanisms [ 46, 47].
The input to our analysis exploits earlier work by Locke et al (unpublished data), in which 50 low cost-of-fit parameter sets were generated following global optimisation to the semi-quantitative cost function (see [1] for details).
The effectiveness of the schemes is evaluated by comparing their optimal expected costs against each other and against the costs of Fixed-parameter (Fp) charts.
Due to logistical constraints and test costs, the range of parameters that can be varied during physical experiments is typically limited.
We used a combination of one-way, and where appropriate, multi-way sensitivity approaches to analyse predicted effects and costs when subsets of parameters were varied.
In this figure the cost of effort parameters are (gamma _{c}=0.001) and ( gamma _{l}=0.00075,) and the liquidation cost parameters are (delta _{c}=0) and (delta _{l}=0.025).
