Unknown unknowns are operational scenarios in a cyber-physical system that
are not accounted for in the design and test phase. As such under
unknown-unknown scenarios, the operational behavior of the CPS is not
guaranteed to meet requirements such as safety and efficacy specified using
Signal Temporal Logic (STL) on the output trajectories. We propose a novel
framework for analyzing the stochastic conformance of operational output
characteristics of safety-critical cyber-physical 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) which is used to check the model
conformance using STL on the model coefficients. We demonstrate the detection
of operational changes in an Artificial Pancreas(AP) due to unknown insulin
cartridge errors