I-Corps: Translation Potential of a Solution to Simulate and Assess Verification Strategies

Award ID: 2429513

The broader impact of this I-Corps project is based on the development of a software tool used for verification strategies in systems engineering. This project introduces a software tool designed to optimize verification strategies within complex systems such as satellites, automobiles, and autonomous vehicles. This innovation is able to enhance efficiency and reduce costs by automating the evaluation and optimization of complex verification processes. The technology aims to elevate the reliability and performance of systems through a refined approach, thereby reducing the likelihood of errors. The scalability and adaptability of the technology make it well-suited to handle the increasing complexity of modern systems, with machine learning algorithms enabling it to evolve and improve over time. In addition to offering a competitive edge in the global market through the development of higher quality systems, the application also promotes sustainable engineering practices by minimizing waste and re-work. By enhancing the verification processes within systems engineering, this technology offers more reliable, efficient, and innovative system development. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The technology merges graph theory, data science, and machine learning to tackle the complexities of verification strategies in systems engineering. This research and development advances knowledge by applying theoretical concepts to practical challenges, enhancing the efficiency, reliability, and manageability of verification processes. The technology also advances the understanding of how computational techniques can augment human cognition and decision-making in the face of complex system dependencies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman