This repository contains the technical report, figures, and conceptual demonstration scripts for: “TLMM v4.5: Continual Adaptive Risk Mapping, Resilience-Aware Structural Dynamics, and Self-Evolving Digital Twin Architecture” TLMM (Threshold-Limited Mode Modulation) v4.5 presents an exploratory mathematical and translational framework for continual adaptive risk mapping under non-stationary stochastic dynamics. The framework integrates: - stochastic Fokker–Planck closure- analytical operational-window narrowing- finite-timescale adiabatic reduction error- exploratory quantitative parameter fitting- inter-subject variability analysis- continual adaptive risk mapping- predictive vs. reactive control comparison- uncertainty-aware adaptive digital twin architecture- resilience-aware structural dynamics- adaptive forgetting and continual learning The central conceptual shift introduced in v4.5 is: “from risk of escape to ability to return.” A resilience metric is introduced to quantify stable recovery capacity under perturbation, while adaptive forgetting mechanisms dynamically adjust learning rates under non-stationary conditions. The repository includes:- full technical report PDF- publication-ready figures (Fig.1–Fig.10)- README documentation- simplified conceptual Python demonstration scripts Important Notes:- This work is exploratory and hypothesis-generating.- CHB–MIT EEG analyses are included solely as proof-of-contact examples.- Predictive vs. reactive control results are based entirely on exploratory Monte Carlo simulations.- No clinical validation, therapeutic claim, medical-device claim, or clinical-decision claim is made. This repository is intended as an exploratory adaptive systems framework connecting stochastic dynamics, continual inference, resilience-aware adaptation, and uncertainty-aware digital twin architectures under non-stationary environments. Author: Koji OkinoDate: May 2026
Koji Okino (2026) studied this question.