The Universal Stability Law by Limited Propagation — Version 4 (Nonlinear Extension)

The Universal Stability Law by Limited Propagation — Version 4 (Nonlinear Extension) This Version 4 introduces the nonlinear extension of the Universal Stability Law by Limited Propagation.It expands the analytical framework to include saturating dynamics (φ(x) = tanh(x)), confirming that the stability frontier remains at F/C = 1, while supercritical systems evolve toward bounded attractors instead of diverging. The document unifies analytical derivation, linear computational validation (Appendix A), and nonlinear simulation (Appendix B).This nonlinear extension connects the Universal Stability Law to realistic systems with feedback saturation, such as neural networks, oscillators, ecological and power systems.It represents the most complete formulation to date of the principle: F < C → stableF = C → criticalF > C → unstable Esta Versión 4 presenta la extensión no lineal de la Ley Universal de Estabilidad por Propagación Limitada.Amplía el marco analítico para incluir dinámicas con saturación (φ(x)=tanh(x)), confirmando que la frontera de estabilidad se mantiene en F/C = 1, mientras los sistemas supercríticos evolucionan hacia atractores acotados en lugar de divergir. El documento integra la derivación analítica, la validación computacional lineal (Apéndice A) y la simulación no lineal (Apéndice B).Esta extensión conecta la Ley Universal de Estabilidad con sistemas reales que presentan saturación — redes neuronales, osciladores, sistemas ecológicos y eléctricos — constituyendo la formulación más completa hasta la fecha del principio: F < C → estableF = C → críticoF > C → inestable

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-20
DOI
https://doi.org/10.5281/zenodo.17393811
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

The Universal Stability Law by Limited Propagation — Version 4 (Nonlinear Extension)

Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

The Universal Stability Law by Limited Propagation — Version 4 (Nonlinear Extension)

article en

Abstract

The Universal Stability Law by Limited Propagation — Version 4 (Nonlinear Extension) This Version 4 introduces the nonlinear extension of the Universal Stability Law by Limited Propagation.It expands the analytical framework to include saturating dynamics (φ(x) = tanh(x)), confirming that the stability frontier remains at F/C = 1, while supercritical systems evolve toward bounded attractors instead of diverging. The document unifies analytical derivation, linear computational validation (Appendix A), and nonlinear simulation (Appendix B).This nonlinear extension connects the Universal Stability Law to realistic systems with feedback saturation, such as neural networks, oscillators, ecological and power systems.It represents the most complete formulation to date of the principle: F < C → stableF = C → criticalF > C → unstable Esta Versión 4 presenta la extensión no lineal de la Ley Universal de Estabilidad por Propagación Limitada.Amplía el marco analítico para incluir dinámicas con saturación (φ(x)=tanh(x)), confirmando que la frontera de estabilidad se mantiene en F/C = 1, mientras los sistemas supercríticos evolucionan hacia atractores acotados en lugar de divergir. El documento integra la derivación analítica, la validación computacional lineal (Apéndice A) y la simulación no lineal (Apéndice B).Esta extensión conecta la Ley Universal de Estabilidad con sistemas reales que presentan saturación — redes neuronales, osciladores, sistemas ecológicos y eléctricos — constituyendo la formulación más completa hasta la fecha del principio: F < C → estableF = C → críticoF > C → inestable

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 48%
Model Reduction and Neural Networks
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The Universal Stability Law by Limited Propagation — Version 4 (Nonlinear Extension) · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS