Mathematics > Numerical Analysis
[Submitted on 2 Jun 2021 (v1), last revised 15 Jul 2021 (this version, v2)]
Title:The Exact Spectral Derivative Discretization Finite Difference (ESDDFD) Method for Wave Models: A Universal Wave View Through Natural Fractional / Fractal Derivative Representations (or View Lens Shops for The Exponential Wave Universe)
View PDFAbstract:A wave view of the universe is proposed in which each natural phenomenon is equipped with its own unique natural viewing lens. A self-sameness modeling principle and its systematic application in Fourier-Laplace transform space is proposed as a novel, universal discrete modeling paradigm for advection-diffusion-reaction equations (ADREs) across non-integer derivatives, time scales, and wave spectral signatures. Its implementation is a novel exact spectral derivative discretization finite difference method (ESDDFD), a way for crafting wave viewing lenses by obtaining discrete wave models from ADRE models. The template for building these lenses come in the form of natural derivative representations obtained from the wave signature probability distribution function and its harmonic oscillation in FL transform space; use of the ESDDFD method in the discrete numerical modeling of wave equations requires no a-priori theory of any mathematical derivative. A major mathematical consequence of this viewpoint is that all notions of the mathematical integer or non-integer derivatives have representation as limits of such natural derivative representations; this and other consequences are discussed and a discretization of a simple integer derivative diffusion-reaction equation is presented to illustrate the method. The resulting view lenses, in the form of ESDDFD models, work well in detecting both local and non-local Debye or Kohlrausch-Williams-Watts exponential patterns; only Brownian motion and sub-diffusion are discussed in the present article.
Submission history
From: Dominic P Clemence [view email][v1] Wed, 2 Jun 2021 13:36:30 UTC (1,184 KB)
[v2] Thu, 15 Jul 2021 17:57:26 UTC (1,181 KB)
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