SIAM Journal on Numerical Analysis · 2005 · 57 citations · 8 references
ArchitectureComputational EngineeringComputational GeometryGeometric ModelingGeometryCurve InterpolationError EstimationGeometry ProcessingShape FunctionsShape InterpolationSurface InterpolationGeometric InterpolationHessian ApproximationInterpolation Error EstimatesLinear InterpolationModeling With Quadratic FunctionsQuadratic ModelsTriangular Surface Interpolation
In this paper, we attempt to reveal the precise relation between the error of linear interpolation on a general triangle and the geometric characters of the triangle. Taking the model problem of interpolating quadratic functions, we derive two exact formulas for the $H^1$-seminorm and $L^2$-norm of the interpolation error in terms of the area, aspect ratio, orientation, and internal angles of the triangle. These formulas indicate that (1) for highly anisotropic triangular meshes the $H^1$-seminorm of the interpolation error is almost a monotonically decreasing function of the angle between the orientations of the triangle and the function; (2) maximum angle condition is not essential if the mesh is aligned with the function and the aspect ratio is of magnitude $\sqrt{|\lambda_1/\lambda_2|}$ or less, where $\lambda_1$ and $\lambda_2$ are the eigenvalues of the Hessian matrix of the function. With these formulas we identify the optimal triangles, which produce the smallest $H^1$-seminorm of the interpolation error, to be the acute isosceles aligned with the solution and of an aspect ratio about $0.8 |\frac{\lambda_1}{\lambda_2}|$. The $L^2$-norm of the interpolation error depends on the orientation and the aspect ratio of the triangle, but not directly on its maximum or minimum angles. The optimal triangles for the $L^2$-norm are those aligned with the solution and of an aspect ratio $\sqrt{|\lambda_1/\lambda_2|}$. These formulas can be used to formulate more accurate mesh quality measures and to derive tighter error bounds for interpolations.
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