General Mechanics

This work presents a framework for mechanics derived from a single principle: the generalized Stokes identity on an informational manifold. By synthesizing this geometric bookkeeping with principles from thermodynamics and information theory, the framework demonstrates how core structures of physics can be reconstructed. The analysis yields the canonical rate equations of mechanics, the inverse-area scaling of boundary fields, and the propagation laws for wave-like transport. The same structure yields operational limits such as time-information complementarity and finite channel capacity. For operational use, a constant-free, one-datum calibration maps the underlying informational field to measured sectors (e.g., gravitational/thermal, electric), rendering the framework predictive while keeping its core purely manifold-theoretic. https://github.com/user-attachments/files/23435236/general-mechanics-2025-11-08.pdf

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-09
DOI
https://doi.org/10.5281/zenodo.17561648
Primary Topic
Statistical Mechanics and Entropy
Type
article
Field-Weighted Citation Impact
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article

General Mechanics

Zenodo (CERN European Organization for Nuclear Research)
Statistical Mechanics and Entropy
article

General Mechanics

article en

Abstract

This work presents a framework for mechanics derived from a single principle: the generalized Stokes identity on an informational manifold. By synthesizing this geometric bookkeeping with principles from thermodynamics and information theory, the framework demonstrates how core structures of physics can be reconstructed. The analysis yields the canonical rate equations of mechanics, the inverse-area scaling of boundary fields, and the propagation laws for wave-like transport. The same structure yields operational limits such as time-information complementarity and finite channel capacity. For operational use, a constant-free, one-datum calibration maps the underlying informational field to measured sectors (e.g., gravitational/thermal, electric), rendering the framework predictive while keeping its core purely manifold-theoretic. https://github.com/user-attachments/files/23435236/general-mechanics-2025-11-08.pdf

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 44%
Statistical Mechanics and Entropy
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