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.

翻译：未知未知（Unknown-unknowns）是指信息物理系统在设计与测试阶段未纳入考量的运行场景。在此类未知未知场景下，CPS的输出轨迹无法保证满足以信号时序逻辑（STL）表述的安全性及有效性等要求。本文提出一种新型框架，通过分析安全关键型信息物理系统的运行输出特性的随机一致性，可发现未知未知场景并评估潜在安全风险。我们提出动力学诱导混合递归神经网络（DiH-RNN），用于挖掘物理引导替代模型（PGSM），并基于该模型系数通过STL验证模型一致性。我们以人工胰腺（AP）为例，展示了因未知胰岛素药筒错误导致的运行状态变化的检测效果。