  Unknown-unknowns are operational scenarios in systems that are not accounted
for in the design and test phase. In such scenarios, the operational behavior
of the Human-in-loop (HIL) Human-in-Plant (HIP) systems is not guaranteed to
meet requirements such as safety and efficacy. We propose a novel framework for
analyzing the operational output characteristics of safety-critical HIL-HIP
systems that can discover unknown-unknown scenarios and evaluate potential
safety hazards. We propose dynamics-induced hybrid recurrent neural networks
(DiH-RNN) to mine a physics-guided surrogate model (PGSM) that checks for
deviation of the cyber-physical system (CPS) from safety-certified operational
characteristics. The PGSM enables early detection of unknown-unknowns based on
the physical laws governing the system. We demonstrate the detection of
operational changes in an Artificial Pancreas(AP) due to unknown insulin
cartridge errors.
