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The algorithm employs parametrization by high order polynomials and a linearization of the weak formulation of the Laplace Beltrami operator to arrive at an iterative procedure to evolve from a given initial surface to the final minimal surface.
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Interconnections are bounded by higher-order polynomials in the decentralized strict feedback form.
In order to enhance accuracy, high order polynomials can be specified as local approximation.
Random regressions require fitting polynomial functions, but larger amounts of well-distributed data are required to fit high order polynomials [ 51].
The displacement fields in this theory are assumed to be piece-wise continuous high order polynomial series, layer by layer or sublaminate by sublaminate in the thickness direction.
It is plausible that a better fit can be obtained by some other function (e.g., high order polynomial), but it is questionable how an increased number of fitting parameters can be connected to actual morphological features.
The advantages of the new strategy can be summarized as 1) the replacement of the cumbersome solution step of high order polynomial equations required by Booth's original method with the simple matrix eigen decomposition, 2) faster fission source convergence in inactive cycles, 3) more stable behaviors in both inactive and active cycles, and 4) smaller variances in active cycles.
But a really high order polynomial is too complex.
High order polynomial functions may be adopted.
Local refinement is applied by introducing higher order polynomials in selected subdomains (local p-refinement) to keep the problem size small while ensuring high approximation accuracy.
Non-linear relations were assessed by adding higher order polynomials to the final multivariable model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com