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The computational sensitivity is discussed.
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Computational sensitivity analysis is developed, which allows for efficient usage of the gradient based optimization methods.
The computational examinations including sensitivity, convergence property, and parallelization are discussed.
In this work, we propose sampling plans for reducing the computational burden of sensitivity estimates while improving and controlling the accuracy in the estimation.
With the experimental validation of the computational predictions, the sensitivity and false positive rate of each program, or program combination, was determined.
The sensitivities are verified to be accurate, while the computational cost to compute the entire sensitivity matrix is equivalent to only one additional plasma edge simulation for each output quantity of interest.
Thus the computational cost of the sensitivity indices practically reduces to that of estimating the PCE coefficients.
In order to improve the computational efficiency, we parallelize sensitivity calculation and achieve a close-to-linear speed up rate.
The main purpose of this work is the computational simulation of the sensitivity coefficients of the homogenized tensor for a polymer filled with rubber particles with respect to the material parameters of the constituents.
For practical direction finding problems, we use the SVD of the data matrix to reduce both the computational complexity and the sensitivity to noise, just like l 1-SVD [7].
MTMcA was responsible for the computational mathematical modeling and sensitivity analysis of the system.
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