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ZENODO
Software . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Software . 2026
License: CC BY
Data sources: Datacite
ZENODO
Software . 2026
License: CC BY
Data sources: Datacite
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FQFT Series Computational Companion: Reproducible Prediction Framework for Papers I–X

Authors: Petrov Pasev, Ivan;

FQFT Series Computational Companion: Reproducible Prediction Framework for Papers I–X

Abstract

Python 3 computational companion for the Fractal Quantum Field Theory (FQFT) research series. This software package reproduces the numerical prediction framework associated with the FQFT Papers I–X program, including spectral-geometry-derived parameter evaluations, hierarchical scaling structures, and comparative prediction tables against contemporary experimental reference datasets. The implementation is designed as a reproducible computational artifact accompanying the formal FQFT theoretical framework. The codebase is lightweight, deterministic, and structured for independent verification using standard scientific Python environments. Core features include: Spectral geometric computation routines Recursive scaling evaluation Fixed-point parameter analysis Prediction table generation Numerical comparison outputs Reproducible execution pipeline The software is intended as the primary reproducibility layer for the FQFT publication stack and serves as the computational counterpart to the formal kernel papers and phenomenological prediction documents. Runtime environment: Python 3 NumPy SciPy This upload forms part of the Fractal Quantum Field Theory (FQFT) research program developed under the Global Institute of Logic & Cybernetics (GILC).

Keywords

FQFT, Computational Physics, Fractal Quantum Field Theory, Mathematical physics, Ramanujan graphs, spectral geometry

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average