From Substrate to Structural Grammar: Realizing Fractal Theory Operators on the Three Force Fractal Model
Abstract
Fractal Theory (FT) presents a compact operator grammar with Unity, Division, Scale, Drift, and Memory, linked by a small set of kernel equations that describe coherence, fragmentation, and structural evolution across domains. The Three Force Fractal Model (3FFM) is a deterministic substrate model built from a finite Node Tapestry, local Dihapl (DH) state at each Node, a global Tick update rule, and temporal cost functionals called Temporal Drag (TD) and Temporal Configuration Drag (TCD). Mass is defined as accumulated TD plus TCD over regions and Tick intervals, and Tagged DH (TDH) worldlines obey locality, finite speed, and cost of motion constraints [1, 2]. This paper constructs an explicit bridge between 3FFM and FT. The main idea is to treat 3FFM as a concrete substrate and to define FT operators as coarse grained, dimensionless functionals of 3FFM data on a chosen region. We work in three stages. A concise summary of the 3FFM substrate, including Tapestry structure, DH states, TD and TCD, mass, TDH worldlines, and barrier bands. A summary of FT and its operator kernel, following the formalization in the Fractal Theory operator kernel document [3]. An explicit construction that maps 3FFM configurations on a region to Unity, Division, Scale, Memory, and Drift, and that realizes the FT kernel equations in discrete time, with a clear route to the continuous limit. The mapping presented here is one consistent choice rather than a unique Canon. It is sufficient to show that FT can be realized as a structural grammar sitting above a specific deterministic substrate, while remaining portable to other possible substrates.
Fields of Study & Subjects
Advanced Mathematical Theories and Applications
Complex Systems and Dynamics
Cellular Automata and Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 10 January 2026
- Document type
- Preprint