  We present a safety verification framework for design-time and run-time
assurance of learning-based components in aviation systems. Our proposed
framework integrates two novel methodologies. From the design-time assurance
perspective, we propose offline mixed-fidelity verification tools that
incorporate knowledge from different levels of granularity in simulated
environments. From the run-time assurance perspective, we propose reachability-
and statistics-based online monitoring and safety guards for a learning-based
decision-making model to complement the offline verification methods. This
framework is designed to be loosely coupled among modules, allowing the
individual modules to be developed using independent methodologies and
techniques, under varying circumstances and with different tool access. The
proposed framework offers feasible solutions for meeting system safety
requirements at different stages throughout the system development and
deployment cycle, enabling the continuous learning and assessment of the system
product.
