Physics-Informed ML for Jet Engine R&D & Anomaly Detection

Accelerating R&D cycles and enhancing reliability are critical for modern airpower. This challenge tasks developers with creating a cutting-edge system for propulsion system health monitoring and R&D optimization. The solution must integrate physics-based models with advanced machine learning techniques to predict component degradation and detect anomalies in real-time telemetry data. Furthermore, participants will leverage Claude 3.5 Haiku, orchestrated with Marvin, for intelligent interpretation of unstructured maintenance logs and R&D reports. Pinecone will serve as a semantic knowledge base, enabling efficient retrieval of historical data to inform predictive models and accelerate the generation of actionable insights for faster, more effective propulsion system development and maintenance decisions.

• Master the principles of Physics-Informed Neural Networks (PINNs) or similar techniques to build models that respect known thermodynamic and mechanical constraints of jet engines for more robust degradation prediction.

• Implement advanced time-series anomaly detection algorithms (e.g., LSTM autoencoders, Isolation Forests, or transformer-based models) on synthetic or real (provided) propulsion telemetry data.

• Design and Build an integration layer using Marvin to connect Claude 3.5 Haiku with unstructured text data from maintenance logs, enabling extraction of structured insights (e.g., failure modes, component wear patterns, environmental factors).

• Orchestrate a data pipeline to convert historical R&D reports, test specifications, and incident logs into vector embeddings suitable for storage and efficient semantic search in Pinecone.

• Develop a Remaining Useful Life (RUL) prediction model for critical engine components (e.g., turbine blades, bearings) leveraging both physics-informed ML outputs and historical failure data.

• Integrate the anomaly detection and RUL prediction systems into a dashboard or alerting mechanism that provides real-time health assessments of a simulated engine fleet.

• Optimize the system's ability to identify correlations between detected anomalies, predicted degradation, and past R&D parameters stored in Pinecone, to accelerate future R&D cycles.