TLMM v5.8 introduces a topology-aware population-scale viability framework integrating Wasserstein manifold aggregation, persistent homology, macro-uncertainty propagation, and unified global viability metrics. The framework extends previous topology-aware digital twin systems toward population-scale viability field representations capable of capturing collective resilience, uncertainty accumulation, topological drift, and structural transitions across interacting populations. Core contributions include: • Topology-preserving Wasserstein manifold aggregation• Persistent homology-based topological drift tracking• Macro-uncertainty propagation across coupled twin networks• Unified Global Viability Index (GVI)• Emergent population-scale viability field visualization• Comparative aggregation analysis• Sensitivity analysis of viability components• Conceptual topology-aware adaptive structural patch mechanisms The work is exploratory and conceptual and aims to provide an interpretable framework for future topology-aware adaptive viability systems and population-level digital twin architectures. Included materials:- Full PDF manuscript- Figure set (Fig.1–Fig.10)- Python exploratory demo script- README documentation Limitations:- No clinical validation is claimed- Figures are illustrative and conceptual- Numerical examples are synthetic- Adaptive repair mechanisms are conceptual only Author:Koji OkinoIndependent Researcher · SD Lab ORCID:https://orcid.org/0009-0003-9273-9813
Koji Okino (Sun,) studied this question.