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Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance.
The conventional linear approximation method requires a simple hardware implementation but produces results with high approximation error.
These schemes give uniformly high approximation order and allow for a spectral-like wave resolution while dissipating non-resolved wavenumbers.
This method enables application of the classical formulation and high approximation accuracy as proven in comparison with published solutions.
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.
In this paper we characterize all totally interpolating biorthogonal finite impulse response (FIR) multifilter banks of multiplicity two, and provide a design framework for corresponding compactly supported multiwavelet systems with high approximation order.
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The Chebyshev meta-model is expected to provide higher approximation accuracy.
However, employing the higher approximation order (i.e. S8 scheme) requires 3.33 times the computational storage compared with the S4 approximation.
The results indicate that CKMF model has higher approximation accuracy with the same high-fidelity samples, and converges at less high-fidelity samples.
Through the experimental analysis, it is demonstrated that the proposed method possesses higher approximation accuracy with relatively fewer rule number in comparison with traditional ones.
The effect of higher approximation order and mesh refinement on the quality of the obtained solution quantities like stress components and displacements, is studied in detail.
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