This repository contains results from the FAA funded program - Safety Assurance in Complex Aerospace Digital Systems that include AI/ML
- gsn: Contains the GSN artifacts produced in the program
- ML: Contains data, code, and ML models used in the program
- SADL: Contains the SADL data models developed in the program
- tools: Contains code for all tools developed
- An Overarching Properties-based GSN synthesis and navigation tool prototype is available here
- Paul, S., Prince, D., Iyer, N., Durling, M., Visnevski, N., Meng, B., Varanasi, S.C., Siu, K., McMillan, C. and Meiners, M., 2023, October. Towards the Certification of Neural Networks using Overarching Properties: An Avionics Case Study. In 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC) (pp. 1-10). IEEE.
- Paul, S., Prince, D., Iyer, N., Durling, M., Visnevski, N. and Meng, B., 2023. Assurance of Machine Learning-Based Aerospace Systems: Towards an Overarching Properties-Driven Approach (No. DOT/FAA/TC-23/54). United States. Department of Transportation. Federal Aviation Administration. William J. Hughes Technical Center.
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This material is based upon work supported by the Federal Aviation Administration (FAA) under Contract No. 692M15-22-T-00012.
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