dorsal/arxiv
View SchemaSwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Models
| Authors | Chunwei Yang, Yankai Wang, Jianxiang Tang, Haojie Qu, Ziqiang Zou, YuLiu, Chunrui Deng, Zhifang Qiu, Ming Ding |
|---|---|
| Categories | |
| ArXiv ID | 2601.07252vv1 |
| URL | https://arxiv.org/abs/2601.07252 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Numerical simulation is one of the mainstream methods in scientific research, typically performed by professional engineers. With the advancement of multi-agent technology, using collaborating agents to replicate human behavior shows immense potential for intelligent Computational Fluid Dynamics (CFD) simulations. Some muti-agent systems based on Large Language Models have been proposed. However, they exhibit significant limitations when dealing with complex geometries. This paper introduces a new multi-agent simulation framework, SwarmFoam. SwarmFoam integrates functionalities such as Multi-modal perception, Intelligent error correction, and Retrieval-Augmented Generation, aiming to achieve more complex simulations through dual parsing of images and high-level instructions. Experimental results demonstrate that SwarmFoam has good adaptability to simulation inputs from different modalities. The overall pass rate for 25 test cases was 84%, with natural language and multi-modal input cases achieving pass rates of 80% and 86.7%, respectively. The work presented by SwarmFoam will further promote the development of intelligent agent methods for CFD.
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"abstract": "Numerical simulation is one of the mainstream methods in scientific research, typically performed by professional engineers. With the advancement of multi-agent technology, using collaborating agents to replicate human behavior shows immense potential for intelligent Computational Fluid Dynamics (CFD) simulations. Some muti-agent systems based on Large Language Models have been proposed. However, they exhibit significant limitations when dealing with complex geometries. This paper introduces a new multi-agent simulation framework, SwarmFoam. SwarmFoam integrates functionalities such as Multi-modal perception, Intelligent error correction, and Retrieval-Augmented Generation, aiming to achieve more complex simulations through dual parsing of images and high-level instructions. Experimental results demonstrate that SwarmFoam has good adaptability to simulation inputs from different modalities. The overall pass rate for 25 test cases was 84%, with natural language and multi-modal input cases achieving pass rates of 80% and 86.7%, respectively. The work presented by SwarmFoam will further promote the development of intelligent agent methods for CFD.",
"arxiv_id": "2601.07252",
"authors": [
"Chunwei Yang",
"Yankai Wang",
"Jianxiang Tang",
"Haojie Qu",
"Ziqiang Zou",
"YuLiu",
"Chunrui Deng",
"Zhifang Qiu",
"Ming Ding"
],
"categories": [
"cs.MA"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Models",
"url": "https://arxiv.org/abs/2601.07252",
"version": "v1"
},
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