Validity-Aware Modeling: Time-Dependent Validity Windows for Measurement, Models, and Long-Term System Integrity

This work introduces a validity-aware modeling framework in which model validity is treated as atime-dependent, decision-relevant state rather than a static assumption. Modern engineered systems increasingly operate near temporal, structural, and operational limits,where delayed measurement, drift, aging, and accumulated approximation errors can silently erodemodel reliability. While these effects are well known individually, they are often not integratedinto a unified governance structure for model usage and decision-making. The framework defines a time-dependent validity function V(t, H, ε) that quantifies how long amodel is expected to remain within tolerance over a future decision horizon. Validity is explicitlyestimated from timestamped measurements, residual history, uncertainty growth, and drift indicators,and is used to govern operational decisions, monitoring intensity, model updates, and usage limits. No new physical assumptions are introduced. The contribution is structural and methodological:a coherent integration of latency-aware estimation, discrepancy tracking, drift detection, anddecision logic into a single, traceable validity state. This paper is intended as a conceptual and methodological reference for engineering, digital twins,AI governance, safety-critical systems, and long-term system integrity. It deliberately avoidsimplementation-specific prescriptions and serves as a foundation for reproducible evaluation,industrial adaptation, and future technical realizations.