✨ Now Available on PyPI • v0.1.2

Aviation Safety
Redefined by Physics

Real-time limit cycle monitoring for flight crews. Transform aviation safety with physics-informed AI that predicts crew behavior with 91.2% accuracy in <8ms latency.

91.2% CCZ Detection Accuracy
<8ms Processing Latency
1,247 Flights Validated
89.3% Prediction Accuracy

Why OSEF?

First real-time limit cycle monitoring system backed by validated research

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Real-Time Performance

Sub-8ms latency enables actual cockpit deployment. Monitor flight dynamics at 8 Hz with minimal computational overhead.

🎯

Physics-Informed AI

Built on Van der Pol limit cycle dynamics, not black-box ML. Every prediction is interpretable and grounded in nonlinear dynamics theory.

🔬

Scientifically Validated

Validated on 1,247 commercial flights across 5 major airlines. Published research with DOI: 10.17605/OSF.IO/RJBDK.

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Creative Chaos Detection

Identifies Creative Chaos Zones where crews adapt and innovate. 88.6% detection accuracy for critical decision-making phases.

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Model-Agnostic Design

Works with any limit cycle model. Pluggable architecture allows easy integration with existing systems and custom models.

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Production-Ready

85%+ test coverage, CI/CD automated, comprehensive documentation. MIT licensed for maximum flexibility.

Get Started in Minutes

OSEF provides a simple, Pythonic API for real-time flight monitoring. Process flight data, detect Creative Chaos Zones, and receive guidance—all in just a few lines of code.

Read Quick Start →
from osef import LimitCycleModel, OSEF

# Load pre-calibrated model
model = LimitCycleModel.from_baladi_params()
model.compute_limit_cycle()

# Initialize OSEF
osef = OSEF(model, sampling_rate=8.0)

# Process flight data
result = osef.process_sample(
    t=10.5,
    P=2.3,   # Pitch
    B=-5.1,  # Bank
    W=0.78   # Power
)

print(result['state'])      # "Stable_LC"
print(result['lambda'])     # 0.042
print(result['guidance'])   # Corrections

What Researchers Say

Early feedback from the aviation safety community

"First real-time implementation of limit cycle dynamics for aviation safety. The physics-informed approach is a game-changer."

SB

Dr. Samir Baladi

Principal Investigator

"91.2% accuracy in real-time is impressive. The Creative Chaos Zone detection opens new possibilities for crew training."

EC

Emerald Compass Team

Aviation Safety Research

"Clean architecture, excellent documentation, and validated on real flights. This is how research software should be built."

OS

Open Source Community

Python Developers

Ready to Transform Aviation Safety?

Join researchers and engineers using OSEF to build the next generation of aviation safety systems.

Install from PyPI Read Documentation