# LLMDrivenSimulation
**Repository Path**: cool_c0de/LLMDrivenSimulation
## Basic Information
- **Project Name**: LLMDrivenSimulation
- **Description**: from https://github.com/YuchenXia/LLMDrivenSimulation 20251105
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-05
- **Last Updated**: 2025-11-05
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# LLM experiments with simulation
This repository contains the accompanying demo video and code for the paper: **LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins**
(Work-in-progress, a preprint manuscript draft is available on arXiv: https://arxiv.org/abs/2405.18092)
## How to mix a container with different ingredients?
### :woman_scientist: :man_scientist: :bar_chart: Human performs experiment:
A demo video with higher resolution: [mix_real_experiment.mov](mix_real_experiment.mov)
### :robot: :desktop_computer: :bar_chart: LLM agent performs simulation
A demo video with higher resolution: [mix_simulation.mov](mix_simulation.mov)
## The system design
The LLM interprets the simulation steps in a cyclic manner, interacting with the data and control interface in a digital environment.
The system is designed to be **independent from a specific LLM**, meaning that any proprietary LLM or open-source LLM can be used to power the system.
The **reasoning capability** is the most essential, and GPT-4 performs significantly better than GPT-3.5 and other open-source models.
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The user provides an objective to the multi-agent system, which then experiments with the simulation to heuristically explore solutions. Finally, the LLM agent provides a summarized solution to parameterize the simulation model.
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## Research Paper
- **Design**: introduces a framework that integrates a multi-agent system with LLMs to interact with a simulation model and find parametrization solutions for a process.
- **Project Status**: it is currently a **work-in-progress** research project and the paper has been presented at **IEEE ETFA 2024** - IEEE International Conference on Emerging Technologies and Factory Automation (10th-13th September 2024, Padova, Italy).
- **Application Area**: we are investigating the LLMs' interaction with more sophisticated simulation models for **industrial automation systems**.
## Source code release
The folder **source_code** contains the source code for reproducibility.
Follow the **source_code/README.md** for the source code to run the prototyp locally.
Licence: CC BY (Attribution)
## The Paper
Details of this work has been documented in a paper in Proceedings of IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA, 10th-13th September 2024, Padova, Italy) and will be published by IEEE soon.
A preprint manuscript draft is available on arXiv:
>[Xia, Y.](https://yuchenxia.github.io/), Dittler, D., Jazdi, N., Chen, H., & Weyrich, M. (2024). **LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins**. https://arxiv.org/abs/2405.18092
```bibtex
@misc{xia2024llm,
title={LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins},
author={Yuchen Xia and Daniel Dittler and Nasser Jazdi and Haonan Chen and Michael Weyrich},
year={2024},
eprint={2405.18092},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## About the Author
Yuchen Xia is a PhD candidate specializing in LLM agents, digital twins, and industrial automation since 2021. You can find more about his work, publications and projects on his personal website.