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Note that the zeroth approximation is very rough indeed in any case: the periodic solution is approximated by a piecewise linear function (see Figure 6).
The domain decomposition method divides the original domain into sub-domains where the solution is approximated by using piecewise high order rational interpolants on a Chebyshev grid points.
The numerical solution is approximated by a time stepping procedure and it has been implemented in the Symmetric Galerkin Boundary Element Method code.
The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions.
In this method, the three-dimensional computational domain is divided into hexahedral elements in which the solution is approximated by a sum of basis functions.
The solution is approximated by truncating the series and the result is sampled at a finite number of points along the perimeter.
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The actual solution was approximated using the LM and PL solution (for the incompressible and nearly-incompressible comparison respectively) on a finer mesh (mesh7), with a cubic interpolation for the displacement and a quadratic interpolation for the pressure.
The rheological properties of the polymer solutions were approximated by the Williamson model.
A system dynamics simulation model consists of a set of integral equations whose solutions are approximated to demonstrate dynamic system behavior, enabling curves of trends over time in outcomes of interest to be explored and compared for future policy options.
This solution is approximate and the accuracy is dependent on the assumed solution.
In the latter case, the analytical solution is approximate but approaches the exact solution gradually when the number of layers increases.
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