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Complete High Dimensional Inverse Characterization of Fractal Surfaces and Volumes

机译:分形表面和体积的完整高维逆表征

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摘要

In the present paper, we are describing a methodology for the determination of the complete set of parameters associated with the Weierstrass-Mandelbrot (W-M) function that can describe a fractal scalar field distribution defined by measured or computed data distributed on a surface or in a volume. Our effort is motivated not only by the need for accurate fractal surface and volume reconstruction but also by the need to be able to describe analytically a scalar field quantity distribution on a surface or in a volume that corresponds to various material properties distributions for engineering and science applications. Our method involves utilizing a refactoring of the W-M function that permits defining the characterization problem as a high dimensional inverse problem solved by singular value decomposition for the so-called phases of the function. Coupled with this process is a second level exhaustive search that enables the determination of the density of the frequencies involved in defining the trigonometric functions participating in the definition of the W-M function. Numerical applications of the proposed method on both synthetic and actual surface and volume data, validate the efficiency and the accuracy of the proposed approach. This approach constitutes a radical departure from the traditional fractal dimension characterization studies and opens the road for a very large number of applications.
机译:在本文中,我们将描述一种确定与Weierstrass-Mandelbrot(WM)函数相关的完整参数集的方法,该方法可以描述由分布在表面或表面的测量或计算数据定义的分形标量场分布。体积。我们的努力不仅受到对精确的分形表面和体积重建的需求的激励,而且还因为需要能够分析地描述与工程和科学的各种材料特性分布相对应的表面或体积中的标量场数量分布应用程序。我们的方法涉及利用W-M函数的重构,该重构允许将特征问题定义为通过针对函数的所谓相位的奇异值分解解决的高维逆问题。与此过程相结合的是第二级穷举搜索,该搜索能够确定参与定义W-M函数定义的三角函数所涉及的频率密度。该方法在合成和实际表面及体积数据上的数值应用,验证了该方法的效率和准确性。这种方法与传统的分形维数表征研究形成了根本性的偏离,并为大量应用打开了道路。

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  • 来源
    《Journal of Computing and Information Science in Engineering》 |2013年第1期|011001.1-011001.9|共9页
  • 作者单位

    Computational Multiphysics Systems Laboratory, Center of Computational Material Science, Naval Research Laboratory, Washington, DC 20375;

    Computational Materials Science Center, George Mason University, Fairfax, VA 22030 Center of Computational Material Science, Naval Research Laboratory, Washington DC, 20375;

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