Validation of Safety Critical Systems

Explore mathematical concepts and algorithms for validating autonomous decision-making systems before deployment in high-stakes applications through this comprehensive Stanford University course. Master the framework for setting up validation problems using model fitting, model validation, and property specification techniques. Delve into sampling-based validation methods including falsification and probability of failure estimation, then advance to formal methods covering reachability analysis and model checking. Study mathematical modeling, temporal logic specifications, optimization-based falsification, Markov chain Monte Carlo, importance sampling, satisfiability, and explainability concepts. Apply these validation techniques to real-world scenarios in air traffic control, autonomous systems, and self-driving cars. Learn from expert instruction including guest lectures from industry professionals at Terra AI and Stanford faculty. Develop skills in system modeling, property specification, falsification through optimization and planning, failure distribution analysis, adaptive importance sampling, and runtime monitoring. Gain practical experience with both linear and nonlinear system reachability analysis, discrete reachability methods, and explainability frameworks essential for safety-critical system validation.