# Detection of Unknown-Unknowns in Human-in-Plant Human-in-Loop Systems Using Physics Guided Process Models

arXiv: https://arxiv.org/abs/2309.02603 · HTML: https://mlsanity.com/paper/2309.02603/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods](https://mlsanity.com/paper/2202.11762/) (2022)
- [Doping Tests for Cyber-Physical Systems](https://mlsanity.com/paper/1904.08641/) (2019)

## Built on by (most cited first, 1 of 1)

- [Detection of Deployment Operational Deviations for Safety and Security of AI-Enabled Human-Centric Cyber Physical Systems](https://mlsanity.com/paper/2601.04605/) (2026)

## Abstract (arXiv)

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.