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The reliability of the approximation is demonstrated via several engineering mechanics/dynamics related numerical examples pertaining to the stochastic beam bending problem, as well as to the response determination of stochastically excited nonlinear oscillators.
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We will explore the consequences in terms of accuracy and reliability of the approximations inherent to both the GLS and WLS method.
For most patterns, this approach improves the reliability of the approximations, even if we still see noticeable differences.
Therefore, it inevitably rises the question about the reliability of the computed approximations.
This paper draws together some important recent results which are designed to improve: (a) the robustness of standard score tests; and (b) the reliability of the asymptotic approximations used for inferential purposes.
The dual constraint relaxation reflects in two parts: on one hand, the objective function based on local region is exploited to replace the pointwise approximation method; on the other hand, considering the variance of local intensity distribution and the reliability of polynomial approximation, we utilize a Gaussian pyramid convolution strategy to devise polynomial fitting.
Intuitively, the average approximation assigns to all the symbols the average reliability of all the symbols, whereas the maximum approximation assigns to all the symbols the reliability of the worst symbol estimate.
Effective a posteriori error bounds for field variables and outputs of interest are crucial for both the efficiency and the reliability of RB approximations.
This insight may shed light on the reliability of approximation schemes for effective stochastic dynamics in protein-only models.
These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models.
The trust region method is used to manage the sequence of the approximation optimization problems based on a reliability index.
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CEO of Professional Science Editing for Scientists @ prosciediting.com