From the Stochastic Embedding Sufficiency Theorem to a Superspace Diffusion Framework
Carolina Garcia, Luc\'ia Perea Dur\'an, Agnese Venezia, Alex Conradie

TL;DR
This paper generalizes Takens' theorem to stochastic systems, enabling non-parametric recovery of physical fields from scalar time series across multiple domains, and introduces a superspace diffusion framework with testable gravitational predictions.
Contribution
It develops a universal inverse methodology for recovering drift and diffusion fields without prior physics assumptions, leading to a superspace diffusion hypothesis and testable gravitational predictions.
Findings
Recovered fundamental constants across domains without prior assumptions.
Established the superspace diffusion hypothesis with a specific stochastic differential equation.
Predicted galactic-scale gravitational acceleration from coarse-grained superspace dynamics.
Abstract
A generalisation of Takens' delay-coordinate embedding theorem to stochastic systems, the Stochastic Embedding Sufficiency Theorem, is an inverse methodology enabling non-parametric recovery of both drift and diffusion fields from scalar time series without prior assumptions about the governing physics. A blind protocol using only time series data is applied to nine domains: classical mechanics, statistical mechanics, nuclear physics, quantum mechanics, chemical kinetics, electromagnetism, relativistic quantum mechanics, quantum harmonic oscillator dynamics, and quantum electrodynamics. Fundamental constants (the Boltzmann constant, the Planck constant, the speed of light, the Fano factor, and the Van Kampen scaling exponent) emerge in both drift and diffusion channels without prior specification. The recovered diffusion coefficients, viewed across domains, constitute an empirical…
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