Dynamics as the Boundary of Identifiability

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Authors: Mikhail Liashkov
Cited by: 0

Abstract

A radical epistemological reinterpretation of classical mechanics through the formal apparatus of dynamic system identification theory is proposed. Using rigorous definitions from Ljung (1999)—data informativeness, persistent excitation, Fisher information matrix, and Hankel rank—it is demonstrated that Newton's laws represent boundaries of information extraction from observations, not ontological statements about reality. The first law is reformulated as data uninformativeness under zero excitation (rank($\bar{F}$) = 0). The second law emerges from asymptotic variance of estimates: mass as the conditioning parameter (Var($\hat{m}$) $\propto m^4$). The third law is interpreted as self-consistency for closed systems with finite Hankel rank. It is shown that momentum is the conserved coefficient at $1/s$ in spectral decomposition, energy is the invariant quadratic norm preserved by norm-preserving evolution operators, and coordinates are indices of spectral modes, with center of mass as the unique minimal-rank parameterization. For rotational dynamics, it is demonstrated that phase loss under rotation transforms Fourier modes into Bessel functions, with Bessel zeros marking fundamental identifiability boundaries (Fisher = 0, Cramér-Rao = $\infty$). The Dzhanibekov effect is reinterpreted as an informational event: temporary loss and stochastic restoration of orientation identifiability, yielding testable predictions about observer-dependence. A parable illustrates how all mechanical concepts emerge from minimal observational capabilities: a physicist in total darkness with seeds, two ears, and a rotating chair reconstructs "space," "mass," and "time" purely from identification constraints.

Fields of Study & Subjects

Statistical Mechanics and Entropy Quantum Mechanics and Applications Control Systems and Identification

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
3 January 2026
Language
EN
Document type
Preprint