CHRONOS-AI

📖 Overview

Temporal Drift Correction Index (TDCI)

"Reality is delayed. CHRONOS-AI synchronizes the truth." — Samir Baladi, April 2026

CHRONOS-AI introduces the first physics-informed AI framework for quantitative characterization and correction of temporal drift in high-velocity scientific monitoring systems — the Temporal Drift Correction Index (TDCI). Built on seven orthogonal physico-informational descriptors spanning Lorentz-analog coupling efficiency, adaptive kinematic resilience, causal signal density, event-tensor navigation fidelity, causal event integrity, temporal drift field fractal dimension, and noise-coherence inhibition.

92.3%

TDCI Accuracy

44-platform cross-validation

94.1%

Failure Detection

False alert: 3.6%

41 days

Early Warning

Mean lead time

3,916

TEUs

10 years · 44 platforms

TDCI

Temporal Drift Correction Index

// TDCI Composite Formula (Equation 3.1 from paper) TDCI = 0.22·γ_eff* + 0.19·E_k* + 0.17·ρ_cs* + 0.14·σ_nav* + 0.12·CEI* + 0.09·D_tau* + 0.07·NCI* // AI Correction with Velocity/Thermal/EM Bias (Equation from paper Section 4.3) TDCI_adj = σ(TDCI_raw + β_vel + β_thermal + β_em) // Python implementation from chronos_ai import TDCIParameters, compute_tdci params = TDCIParameters( gamma_eff=0.24, e_k=0.83, rho_cs=0.31, sigma_nav=0.73, cei=0.88, d_tau=1.84, nci=0.39 ) result = compute_tdci(params, environment='particle_accelerator')

7 Parameters

Seven Physico-Informational Descriptors

Parameter	Description	Weight	Domain
γ_eff	Lorentz-Analog Coupling Efficiency	22%	Relativistic Kinematics
E_k	Adaptive Kinematic Resilience Coefficient	19%	Thermomechanical Dynamics
ρ_cs	Causal Signal Density	17%	Causal Information Theory
σ_nav	Event-Tensor Navigation Fidelity	14%	Spatio-Temporal Mechanics
CEI	Causal Event Integrity Index	12%	Temporal Coherence Analysis
D_tau	Temporal Drift Field Fractal Dimension	9%	Fractal Temporal Geometry
NCI	Noise-Coherence Inhibition Index	7%	Measurement Degradation

AI Architecture

Physics-Informed Neural Network + Neural ODE

// PINN penalty layer constraints (from paper Section 4.3) // • Causality: information cannot propagate faster than local signal velocity // • Lorentz covariance: correction must transform correctly under reference frame change // • Temporal symmetry preservation: time-reversal symmetry in non-dissipative regimes // Python implementation from chronos_ai import ChronosAIPredictor predictor = ChronosAIPredictor() result = predictor.predict(causal_coherence_data, current_params)

Validation Scope

Five Extreme Kinematic Environments

93.9%

Particle Accelerator

0.9999c–0.99999c · 4–300K · 10 platforms

92.8%

Hypersonic Telemetry

Mach 5–25 · 300–11,000K · 9 platforms

94.7%

Deep-Ocean Acoustic

1,480–1,520 m/s · 2–25°C · 11 platforms

90.1%

Quantum Relay

c · -40–80°C · 8 platforms

91.6%

Polar Seismic

2,000–8,000 m/s · -70–10°C · 6 platforms

📦 Installation

Quick setup

# Clone repository git clone https://github.com/gitdeeper11/CHRONOS-AI.git cd CHRONOS-AI # Run correction python bin/run_correction.py --environment particle_accelerator # Verify installation python -c "from chronos_ai import __version__; print(__version__)"

🔧 API Reference

Python interface

TDCIParameters

Seven physico-informational descriptor container

from chronos_ai import TDCIParameters params = TDCIParameters( gamma_eff=0.24, e_k=0.83, rho_cs=0.31, sigma_nav=0.73, cei=0.88, d_tau=1.84, nci=0.39 )

compute_tdci

TDCI computation with environment-specific normalization

from chronos_ai import compute_tdci result = compute_tdci(params, environment='particle_accelerator') print(result.value) # TDCI value print(result.status) # EXCELLENT/GOOD/MODERATE/CRITICAL/COLLAPSE

ChronosAIPredictor

AI predictor with PINN constraints, Neural ODE, and SHAP explanation

from chronos_ai import ChronosAIPredictor predictor = ChronosAIPredictor() prediction = predictor.predict(causal_coherence_data, current_params) print(prediction.days_to_failure) # Early warning days

🧩 Core Modules

CHRONOS-AI architecture

parameters.py

7 Parameters

γ_eff, E_k, ρ_cs, σ_nav, CEI, D_tau, NCI

tdci.py

TDCI

Composite formula + corrections

predictor.py

Predictor

AI prediction with PINN + Neural ODE

monitor.py

Monitor

Real-time monitoring system

ai/

AI Models

Causal CNN, XGBoost, Neural ODE, PINN

bin/

CLI

run_correction.py, reports

👤 Author

Principal investigator

⏳

Samir Baladi

Interdisciplinary AI Researcher — Temporal Physics & Computational Information Science Division

Ronin Institute / Rite of Renaissance

Samir Baladi is an independent researcher affiliated with the Ronin Institute, developing the Rite of Renaissance interdisciplinary research program. CHRONOS-AI is a physics-informed AI framework for temporal drift correction in high-velocity scientific monitoring systems, integrating relativistic kinematics, causal coherence theory, fractal temporal geometry, Neural ODEs, and PINN architecture.

No conflicts of interest declared. All code and data are open-source under MIT License.

📧 gitdeeper@gmail.com 🔗 ORCID: 0009-0003-8903-0029 🐙 GitHub 🦊 GitLab

📝 Citation

How to cite

@software{baladi2026chronosai, author = {Samir Baladi}, title = {CHRONOS-AI: Temporal Physics-Informed Neural Networks for Relativistic Data Correction in High-Velocity Scientific Monitoring Systems}, year = {2026}, version = {1.0.0}, publisher = {Zenodo}, doi = {10.5281/zenodo.19653388}, url = {https://doi.org/10.5281/zenodo.19653388}, note = {Physics-Informed AI Framework for Temporal Coherence} }

"Temporal event networks in extreme kinematic environments are not passive measurement instruments — they are active information processing systems that sense, integrate, respond to, and transmit information about kinematic state across temporal scales from nanoseconds to months with 92.3% accuracy."