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In computational electromagnetics, there were many attempts to reduce the numerical anisotropy, by applying various techniques.
Over the years, several attempts to reduce the numerical anisotropy by various techniques were reported.
One can reduce the numerical dispersions by optimizing the constant coefficients of the finite-difference operator.
Various attempts to reduce the numerical anisotropy in finite differencing applied to various model equations were presented and discussed.
They have a mechanistic interpretation and allow for analytic manipulations that greatly reduce the numerical cost of likelihood evaluation.
The second-order method is found to reduce the numerical diffusion of QDS as demonstrated in several verification studies.
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At issue is a provision of the landmark 1996 law that reduces the numerical work requirements for states as they move more and more people from welfare to work.
Optimizations of such filters in terms of reducing the numerical anisotropy is also another future area of research.
In general, the use of frozen coefficients reduces the numerical errors.
Moreover, the LFXLMS reduces the numerical error in the finite precision implementation and limits the output power of the secondary source to avoid nonlinear distortion.
The results imply that the SSD scheme significantly reduces the numerical diffusion in contrast to the conventional upwind-biased schemes.
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