Support Vector Analysis for Computational Risk Assessment, Decision-Making, and Vulnerability Discovery in Complex Systems

This project addressed limitations in current probabilistic risk assessment (PRA) by combining a support vector machine and PRA software to auto-detect system design vulnerabilities and find previously unseen issues, reduce human error, and reduce human costs. This method does not require training data that would only be available in the event of system or subsystem failures.