Data dependent Shepard approximation through and adaptive modification of the shape parameter
通过形状参数的自适应修改实现数据依赖的Shepard逼近
José Kuruc, Juan Ruiz-Álvarez, Bo Wang, Dionisio-Félix Yáñez
AI总结 提出一种数据依赖的Shepard插值方法,通过自适应调整形状参数减少一维和二维数据中跳跃间断附近的模糊,理论证明并数值验证了其有效性。
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在本文中,我们介绍了一种新颖的数据依赖Shepard插值方法,该方法受[2]中提出的自适应策略启发。由于Shepard插值不会产生振荡,我们的方法核心目标是减少一维和二维数据中跳跃间断附近的模糊。虽然[2]中的原始工作侧重于径向基函数(RBF)插值,但我们通过引入数据依赖的自适应机制将这些思想扩展到Shepard框架。具体来说,我们通过基于局部光滑指标自适应调整影响权重来修改经典Shepard插值,这些指标修改形状参数。这些指标与[2]中使用的类似,旨在检测间断:对于基于网格的数据,我们使用平方未分割二阶差分;对于散乱数据,我们使用拉普拉斯算子的平方最小二乘近似,按模板点平均局部间隔的平方缩放。由此产生的数据依赖加权方案使得接近间断的核函数表现得像局部delta函数,有效减少了经典Shepard方法引入的间断模糊。我们建立了该方法的理论基础,包括新插值的性质,并从理论上证明了减少间断模糊的可能性。一维和二维数值实验证实,所提出的数据依赖Shepard插值在保持光滑区域高精度的同时,显著减少了跳跃间断的模糊。
In this article, we introduce a novel data-dependent Shepard interpolation method inspired by the adaptive strategies proposed in [2]. In this case, as Shepard interpolation does not produce oscillations, our approach has the core objective of reducing the smearing near jump discontinuities in the data in one and two dimensions. While the original work in [2] focuses in on Radial Basis Function (RBF) interpolation, we extend these ideas to the Shepard framework by incorporating a data-dependent adaptation mechanism. Specifically, we modify the classical Shepard interpolation by adaptively adjusting the influence weights based on local smoothness indicators that modify the shape parameter. These indicators, similar to those used in [2], are designed to detect discontinuities: for grid-based data, we use squared undivided second-order differences, and for scattered data, we employ squared least-squares approximations of the Laplacian scaled by the square of the mean local separation of stencil points. The resulting data-dependent weighting scheme forces the kernels close to a discontinuity to behave like a local delta function, effectively reducing the smearing of the discontinuities introduced by the classical Shepard approach. We establish the theoretical foundation of the method, including the properties of the new interpolation and we theoretically prove that the reduction of the smearing of discontinuities is possible. Numerical experiments in one and two dimensions confirm that the proposed data-dependent Shepard interpolation significantly reduces the smearing of jump discontinuities while maintaining high accuracy in smooth regions.