A Predictive Roadmap for Stochastic Scaling Exponents Across Universality Classes: Identifying the Mathematical Gaps

Official public reference library:GitHub repository: https://github.com/devinromberger123-prog/rgl-scalingSoftware DOI: https://doi.org/10.5281/zenodo.19212391 As of current, the combined 12 articles that were posted have a total of 754 views and 475 downloads within 1 month or less of being public. I truly hope this has been helpful to your work. I will be the first to admit that I am a private researcher without grant funding, doing this on my own time and money. I do not have the privilege of publishing in large peer-reviewed journals (yet). For these reasons, are why I chose to publish here. Please forgive me as I know everything may not be perfect, but I will actively make corrections. I sincerely thank you all for taking the time to review my work. If anyone would be willing to sponsor my work, and/or even be willing to collaborate, I would be extremely grateful. If not, all I ask is to give credit where credit is due. Please reach me personally at http://linkedin.com/in/devin-romberger-92385420a. My goal was to make this work open source to the public. Now, please respect that. Please do the right thing. Reference your sources. Thank you. --------------------------------------------------------------------------------------------------------------------------------------------- This memorandum serves as a predictive roadmap for the application of the universal bivariate scaling law, V_eff(μ, σ) = μ^γ * F(σ / μ^κ), across divergent topological routes to chaos. By isolating the universal scaling architecture from class-specific geometric constants, we identify the solved eigenvalues for established universality classes and explicitly define the unmapped stochastic eigenvalues required to resolve higher-order and non-unimodal critical transitions. A validation taxonomy is provided to delineate confirmed empirical baselines from theoretical predictions. Keywords: Universality Classes Renormalization Group Theory Scaling Exponents Stochastic Perturbation Critical Transitions Bifurcation Theory Nonlinear Dynamics

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-02
DOI
https://doi.org/10.5281/zenodo.19187146
Primary Topic
Data Analysis with R
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article
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article

A Predictive Roadmap for Stochastic Scaling Exponents Across Universality Classes: Identifying the Mathematical Gaps

Zenodo (CERN European Organization for Nuclear Research)
Data Analysis with R
article

A Predictive Roadmap for Stochastic Scaling Exponents Across Universality Classes: Identifying the Mathematical Gaps

article en

Abstract

Official public reference library:GitHub repository: https://github.com/devinromberger123-prog/rgl-scalingSoftware DOI: https://doi.org/10.5281/zenodo.19212391 As of current, the combined 12 articles that were posted have a total of 754 views and 475 downloads within 1 month or less of being public. I truly hope this has been helpful to your work. I will be the first to admit that I am a private researcher without grant funding, doing this on my own time and money. I do not have the privilege of publishing in large peer-reviewed journals (yet). For these reasons, are why I chose to publish here. Please forgive me as I know everything may not be perfect, but I will actively make corrections. I sincerely thank you all for taking the time to review my work. If anyone would be willing to sponsor my work, and/or even be willing to collaborate, I would be extremely grateful. If not, all I ask is to give credit where credit is due. Please reach me personally at http://linkedin.com/in/devin-romberger-92385420a. My goal was to make this work open source to the public. Now, please respect that. Please do the right thing. Reference your sources. Thank you. --------------------------------------------------------------------------------------------------------------------------------------------- This memorandum serves as a predictive roadmap for the application of the universal bivariate scaling law, V_eff(μ, σ) = μ^γ * F(σ / μ^κ), across divergent topological routes to chaos. By isolating the universal scaling architecture from class-specific geometric constants, we identify the solved eigenvalues for established universality classes and explicitly define the unmapped stochastic eigenvalues required to resolve higher-order and non-unimodal critical transitions. A validation taxonomy is provided to delineate confirmed empirical baselines from theoretical predictions. Keywords: Universality Classes Renormalization Group Theory Scaling Exponents Stochastic Perturbation Critical Transitions Bifurcation Theory Nonlinear Dynamics

Zenodo (CERN European Organization for Nuclear Research)
Family of Faith College (US)
Partnerships for the goals
Openalex Percentile: Top 14%
Data Analysis with R
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A Predictive Roadmap for Stochastic Scaling Exponents Across Universality Classes: Identifying the Mathematical Gaps · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS