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arXiv 2602.01707math.NAcs.NAmath.MG
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保持曲率的分形插值函数:一种混合几何方法

Curvature Preserving Fractal Interpolation Functions: A Hybrid Geometric Approach

Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning(斯里·萨蒂亚·赛高等学习学院数学与计算机科学系)

K R Tyada

AI总结:

本文提出一种保持曲率的分形插值函数,通过优化参数以更准确地保持数据曲率,适用于几何建模和科学数据插值。

摘要:

分形插值函数(FIFs)通过迭代函数系统(IFS)生成,为建模自相似和不规则数据提供了强大框架,但传统构造往往忽视几何保真度如曲率。本文介绍了一种基于经典三次样条插值的保持曲率的FIF变体。我们定义了一个曲率感知的迭代函数系统(IFS),其参数通过惩罚方法优化,以最小化与经典样条曲率的偏差。推导了插值和曲率近似的理论条件。我们比较了所提出FIF的曲率与经典三次样条和离散数据曲率在多个例子中的表现。我们的方法实现了数据插值和形状保真,比标准样条更准确地保持曲率。该方法在几何建模、计算机图形学和科学数据插值中具有潜在应用。

英文摘要:

Fractal interpolation functions (FIFs) generated using iterated function systems (IFS) provide a powerful framework for modeling self-similar and irregular data, yet traditional constructions often neglect geometric fidelity such as curvature. In this paper, we introduce a curvature-preserving variant of FIFs built upon a classical cubic spline interpolant. We define a curvature-aware iterated function system (IFS) with parameters optimized via a penalty-based approach to minimize deviation from the curvature of the classical spline. Theoretical conditions for interpolation and curvature approximation are derived. We compare the curvature of the proposed FIF with that of the classical cubic spline and discrete data curvature across multiple examples. Our method achieves both data interpolation and shape fidelity, preserving curvature more accurately than standard splines. The approach has potential applications in geometric modeling, computer graphics, and scientific data interpolation.

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