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For instance, Hoffman et al. give a distributed approximating functional method to solve Burgers' equation for relatively large Reynolds numbers in [6], where a nonlinear transform is applied to generating a nonuniform mesh so that tiny step sizes are given near the boundary to catch the satisfying approximation of shock waves.
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The Fokker– Planck equation is applied to generate a general first order differential equation in the dynamic moments of response co-ordinates.
The training and target data sets are applied to generate a neural network classifier.
Given an image to be inpainted, anisotropic diffusion is initially applied to generate a cartoon image.
A set of rules is applied to generate a recycle superstructure for every identified sequence.
Gradual Evolution Step is applied to generate a new structure for further improvement.
In this case, MFA technique is applied to generate a good initial solution for SA.
The procedure is applied to generate a reduced collection of classifiers that provides a cover set for all classifiers.
After qPCR, linear regression analysis was applied to generate a calibration curve for each of the genes.
Extensive data profiling was applied to generate a well characterised data set (Pfizer dataset collection and profiling - Methods).
An iterative algorithm was applied to generate a parameter print index (PI) used for print technology discrimination.
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