• 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.

All Rights Reserved

This material is based upon work supported by the Federal Aviation Administration (FAA) under Contract No. 692M15-22-T-00012.

This website represents research work funded by the Federal Aviation Administration (FAA) and it is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. The U.S. Government does not endorse products or manufacturers. Trade or manufacturers’ names appear herein solely because they are considered essential to the objective of this presentation/paper. The findings and conclusions are those of the author(s) and do not necessarily represent the views of the funding agency. This document does not constitute FAA policy.