Summary of Metaopenfoam: An Llm-based Multi-agent Framework For Cfd, by Yuxuan Chen et al.
MetaOpenFOAM: an LLM-based multi-agent framework for CFD
by Yuxuan Chen, Xu Zhu, Hua Zhou, Zhuyin Ren
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Fluid Dynamics (physics.flu-dyn)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary MetaOpenFOAM is a novel multi-agent collaboration framework that uses large language models (LLMs) to automate computational fluid dynamics (CFD) simulation tasks. By harnessing the power of MetaGPT’s assembly line paradigm, MetaOpenFOAM breaks down complex CFD tasks into manageable subtasks and assigns diverse roles to various agents. Additionally, Langchain integrates Retrieval-Augmented Generation (RAG) technology, which enhances the framework’s ability by integrating a searchable database of OpenFOAM tutorials for LLMs. The framework demonstrated a high pass rate per test case (85%) with an average cost of $0.22 on a benchmark for natural language-based CFD solver consisting of eight simulation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MetaOpenFOAM is a computer program that helps solve complex problems in fluids and gases. It’s like a team of experts working together to get the job done! This program uses special “big language models” to understand what we want it to do, and then breaks down the task into smaller parts that different agents can work on. It’s very good at solving these kinds of problems, and it can even correct mistakes as it goes along. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
