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Numerical simulations show that this small number of training points leads to accurate ROMs within a large parameter range.
The slope corresponding to and the spacing of ΔV bg ≈ 0.1 V are in good agreement with Figure 2, and lead to the conclusion that we observe single quantum dot behaviour over a large parameter range.
However, as time elapses, the maximal relative error goes down to the order of 10−4 and therefore, the dominant physical properties are accurately captured over a large parameter range.
We find that this approach provides an accurate description in a large parameter range provided the photon tunneling is low enough such that the fields in the outer resonators do not deviate significantly from coherent ones.
In this paper we investigate how the character of the two-dimensional map changes over a large parameter range as the system is driven from a non-impacting orbit to an impacting orbit.
In developing pMOR methods for fast UQ of nanoelectronic, electro-thermal models with random variables or stochastic processes, we applied pMOR techniques to a Power-MOS device (Figure 14), provided by MAG, and obtained a parametric reduced order model (pROM) that is of high accuracy over a very large parameter range.
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The parameters were changed on a logarithmic scale to ensure fine initial stepping and, at the same time, sufficient coverage of potentially very large parameter ranges.
First, large parameter ranges are often found, leading to uncertainty about where realistic parameter values lie.
Hence if we are able to take higher quantum fluctuations in resonator 2 into account the results should become more exact and be applicable to a larger parameter range.
Of course this leads to a larger parameter range than simple importance reweighting of a single chain, hence Eq. (9) cannot be applied directly to the mixture.
Consistent with this view, Figure 4 shows that the linear regime of periodic selection is extended to a larger parameter range as the long-range migration rate is increased.
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