# High Fidelity Fast Simulation of Human in the Loop Human in the Plant (HIL-HIP) Systems

arXiv: https://arxiv.org/abs/2309.06558 · HTML: https://mlsanity.com/paper/2309.06558/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)

## Built on by (most cited first, 2 of 2)

- [Recovering implicit physics model under real-world constraints](https://mlsanity.com/paper/2412.02215/) (2024)
- [STORM: Strategic Orchestration of Modalities for Rare Event Classification](https://mlsanity.com/paper/2412.02805/) (2024)

## Abstract (arXiv)

Non-linearities in simulation arise from the time variance in wireless mobile networks when integrated with human in the loop, human in the plant (HIL-HIP) physical systems under dynamic contexts, leading to simulation slowdown. Time variance is handled by deriving a series of piece wise linear time invariant simulations (PLIS) in intervals, which are then concatenated in time domain. In this paper, we conduct a formal analysis of the impact of discretizing time-varying components in wireless network-controlled HIL-HIP systems on simulation accuracy and speedup, and evaluate trade-offs with reliable guarantees. We develop an accurate simulation framework for an artificial pancreas wireless network system that controls blood glucose in Type 1 Diabetes patients with time varying properties such as physiological changes associated with psychological stress and meal patterns. PLIS approach achieves accurate simulation with greater than 2.1 times speedup than a non-linear system simulation for the given dataset.