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The differences in the measured instantaneous RMS delay spread leads to obtaining different PDFs that fit the measured data where Generalized Pareto distribution is dominant.
How to validate simulation results is considered thoroughly under no analytical and measured data, where a double-check criterion is designed for our simulation approach.
The work is motivated by the need of identifying parameters for complex problems from measured data, where it is a priori not clear which data are to choose.
It is shown that LES is able to deliver good results very close to measured data, where the flow is governed by large, turbulent structures.
First of all, one can choose between simulated or measured data, where simulated data and measured phantom studies have the advantage of knowing the true distribution.
We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed.
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Longitudinal data constitute a common type of repeated measures data, where the observations are ordered by time or position in space.
In general, longitudinal data can be defined as repeated measures data where the observations within entities could not have been randomly assigned to the levels of a "treatment" of interest (usually time or position in space); hence, serial correlation results.
The results show that combustion-LES is able to provide predictions very close to measured data for configurations where the flow is governed by large turbulent structures.
We can therefore perform zooms in the measured data - and where the data are densely sampled, we can obtain high-resolution details.
Besides a visual comparison we calculated the normalized squared deviation, χ, between fit result and measured data according to where NB signifies the total number of tracked beads with displacements (measured) and (calculated).
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