The mathematical foundations of science and rational thinking. From applied mathematics and data analytics to statistical models, topology, and chaos theory—discover contemporary research, insights, and analysis through an interdisciplinary lens.
The science of fractal statistics must be built directly upon fractal analysis, transcending the linear and Gaussian assumptions of classical statistics. The goal here is to mathematically describe and measure the scale-dependent, self-similar, multifractal structures in nature and systems.
A step-by-step guide to Fractal Calculus. These notes include both the mathematical background and physical interpretations.
Fractal Cardinality Theory is the mathematical extension of my “fractal origin logic” — that is, it defines the relationship between the magnitude of numbers (cardinality) and the scalar repetition of existence. This theory reinterprets the concept of “infinity” in classical set theory: infinity is no longer a magnitude, but the sum of self-similar origins.
The geometric expression of the spiral fractal derivative is ready. In this visual, the concept of the derivative is shown in layers within a spiral fractal structure: each nested spiral represents a higher order of the derivative. Moving from the outside in, derivative differences such as Δ𝑓, Δ2𝑓, Δ3𝑓 are represented by shrinking spiral segments. This approach visualizes the classical definition of the derivative (𝑓’ (𝑥) = limΔ𝑥→0 Δ𝑓/Δ𝑥) within a fractal spiral motif, providing both analytical and geometric integrity.
CONTENTS: Fractal Taylor Series Visual Fractal Taylor In this diagram, the fractal extension of the classical Taylor expansion is visualized, where derivative terms become scale-dependent through self-similar modulations. Fractal Laplace Transform Visual This graph shows the fractal extension of the classical Laplace transform: damping behavior on the amplitude axis and self-similar resonances on the frequency
Fractal series expansions are the redefined forms of classical Taylor, Maclaurin, and Fourier series using the principle of self-similarity. The aim here is to capture not only the local behavior of functions but also their fractal resonances that repeat at every scale.
In quantum fractal analysis, potential functions are the extension of classical quantum potential energy with fractal scale dependence. The aim is to model energy resonances at both micro and macro levels by analyzing the probability waves of particles within a fractal space-time structure.
While defined by self-similarity and scale invariance in classical mathematics, the quantum fractal exponential function combines this structure with quantum wave functions, revealing fractal resonance in probability distributions. The side-by-side graphs in the visual show a comparative view of the deterministic repetition of the classical fractal exponential function and the wave-particle interactive, luminous fractal structure of its quantum version.
7- Let’s expand the fractal analysis chain with fractal probability distributions (𝑷𝒇). This is a motif-repeating, multi-scale version of classical probability theory and provides entirely new definitions for uncertainty, risk, and variational systems. Classical Probability Distribution The classical probability density for a random variable 𝑋: 𝑃(𝑥) ≥ 0, ∫-∞∞ 𝑃(𝑥) 𝑑𝑥 = 1 It is a
The logarithmic multifractal model describes the structure of the universe not as uniform, but in the form of multi-scale spiral–fractal rings.