CHRONOS-AI: Temporal Physics-Informed Neural Networks for Relativistic Data Correction in High-Velocity Scientific Monitoring Systems

CHRONOS-AI is a physics-informed artificial intelligence framework designed for the quantitative characterization and correction of temporal drift in high-velocity scientific monitoring systems. The framework introduces the Temporal Drift Correction Index (TDCI), a composite multi-parameter metric integrating seven physico-informational descriptors, including Lorentz-analog coupling efficiency, adaptive kinematic resilience, causal signal density, event-tensor navigation fidelity, causal event integrity, temporal drift fractal geometry, and noise-coherence inhibition. CHRONOS-AIFullPaper. pdf Unlike conventional post-processing correction approaches, CHRONOS-AI operates as a real-time predictive system constrained by physical laws, including relativistic transformations and causality principles. It leverages physics-informed neural networks (PINNs), neural differential equations, and causal convolutional architectures to ensure physically consistent temporal corrections under extreme kinematic, thermal, and electromagnetic conditions. CHRONOS-AIFullPaper. pdf The framework is validated on a large-scale dataset of 3, 916 temporal event units (TEUs) collected across 44 experimental platforms over a 10-year period (2015–2025), spanning five extreme environments including particle accelerators, hypersonic telemetry, deep-ocean acoustic arrays, quantum communication systems, and polar seismic networks. CHRONOS-AIFullPaper. pdf CHRONOS-AI achieves: 92. 3% prediction accuracy for temporal coherence collapse 94. 1% failure detection rate with low false alerts Mean early warning lead time of 41 days before system-level data failure The framework establishes temporal event networks as measurable physico-informational systems and provides a unified methodology for causal event reconstruction, spatio-temporal coherence prediction, and engineering-level intervention guidance. CHRONOS-AIFullPaper. pdf This project contributes to the emerging field of physics-informed AI for extreme environments, enabling next-generation monitoring systems in domains such as particle physics, space communication, climate monitoring, and high-speed aerospace systems.